Biomarker-enhanced Artificial Intelligence-Based Pediatric Sepsis Screening Tool Towards Early Recognition and Personalized Therapeutics

项目摘要 这项拟议的STTR工作的总体目标集中在商业化的 基于生物标志物增强型人工智能（AI）的儿科败血症筛查工具（PSCT）， 纳入急诊科（艾德）工作流程，以提高早期识别和及时启动， 积极的个性化脓毒症治疗 脓毒症的早期识别和及时的个性化管理仍然是最大的挑战之一， 儿科急诊医学早期艾德识别已建立或即将发生的严重脓毒症受到阻碍 常见发热性感染的高患病率，鉴别特征的特异性差，儿童的能力 以补偿直到休克晚期，并延迟/有限的感染敏感性确认 微生物测试在最近的一项研究中，ICU收治的700万败血症病例中有47%的培养结果为阴性。 需要改进的诊断方法来区分无菌性炎症、病毒感染和细菌感染。 疑似脓毒症患者的感染。虽然有用，但常用的基于实验室的诊断方法，如 WBC、CRP、PCT和乳酸在儿科败血症管理中的效用有限。新型生物标志物组 儿科脓毒症生物标志物风险模型（PERSEVERE）已被证明可有效预测 免疫功能低下患者的恶化和死亡率。PERSEVERE生物标志物在 更多未分化的可能患有脓毒症的儿童人群，目的是确定那些 恶化的可能性仍然未知。 脓毒症患者，特别是危重患者，代表了一个高度异质性的人群。主持人的角色- 脓毒症病理生理学中的特异性免疫反应失调，以及 表型，强调需要一种精确的医学PSCT方法，以确定患者谁是最 可能受益于有针对性的干预措施，如限制性液体复苏， 而不是重复的液体推注。 当今市场上的自动化脓毒症筛查工具通常是脆弱的，嵌入大型EHR中的模块 表现出较差特异性和阳性预测值的系统忽略了自由文本中可用的重要证据 注释，并且不反映专家艾德医师在开始脓毒症护理时使用的决策标准。我们 我认为，非常需要一种不断学习的商业PSCT，它利用广泛可用的 EHR接口标准，提供专家知识，生物标志物，NLP和 机器学习增强早期儿科败血症识别并检测可预测治疗的表型 对个性化医疗的反应/结果。 .

PROJECT SUMMARY The overall objective of this proposed STTR effort is focused on the derivation and validation of a commercialized biomarker-enhanced artificial intelligence (AI)-based pediatric sepsis screening tool (PSCT) that can be incorporated into emergency department (ED) workflows to enhance early recognition and the initiation of timely, aggressive personalized sepsis therapy. The early recognition and timely personalized management of sepsis remain among the greatest challenges in pediatric emergency medicine. The early ED recognition of established or impending critical sepsis is hampered by high prevalence of common febrile infections, poor specificity of discriminating features, capacity of children to compensate until advanced stages of shock, and delays/limited sensitivity of infection confirming microbiological tests. In a recent study, 47% of 7 million cases of sepsis admitted to ICUs had negative cultures. Improved diagnostics are needed to distinguish between sterile inflammation, viral infection, and bacterial infection in patients with suspected sepsis. While useful, commonly used laboratory-based diagnostics such as WBC, CRP, PCT and lactate of limited utility in the management of pediatric sepsis. Novel panels of biomarkers for the Pediatric Sepsis Biomarker Risk Model (PERSEVERE) have shown to be effective in prediction of deterioration and mortality in immunocompromised patients. The performance of PERSEVERE biomarkers in a more undifferentiated population of children with possible sepsis, where the aim is to identify those that are about to deteriorate remains unknown. Septic patients, especially when critically ill, represent a highly heterogenous population. The role of the host- specific dysregulated immune response in the pathophysiology of sepsis, coupled with the diversity of phenotypes, highlights the need for a precision medicine PSCT approach that identifies patients who are most likely to benefit from targeted interventions such as restrictive fluid resuscitation where early vasoactive therapy is initiated rather than repeated fluid boluses. Automated sepsis screening tools in the market today are generally brittle, embedded modules in a large EHR system that exhibit poor specificity and positive predictive value, ignore important evidence available in free text notes, and do not reflect decision-making criteria used by expert ED physicians in initiating sepsis care. We believe there is a significant need for a continuously learning commercial PSCT that leverages widely available EHR interface standards to deliver the combined analytic power of expert knowledge, biomarkers, NLP and machine learning to enhance early pediatric sepsis recognition and detect phenotypes that can predict treatment responses/outcomes towards personalized medicine. .