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

基于生物标记增强人工智能的儿科败血症筛查工具，实现早期识别和个性化治疗

• 批准号: 10579339
• 负责人: Ioannis Koutroulis
• 金额: $ 29.81万
• 依托单位: COMPUTER TECHNOLOGY ASSOCIATES, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579339
• 关键词: Accident and Emergency department; Admission activity; Agreement; Artificial Intelligence; Artificial Intelligence platform; Bacterial Infections; Biological Markers; Biological Testing; Blood specimen; Bolus Infusion; Caring; Characteristics; Child; Child health care; Childhood; Clinical; Cluster Analysis; Collaborations; Compensation; Consensus; Consent; Coupled; Critical Illness; Dangerousness; Data; Data Set; Decision Making; Decision Trees; Derivation procedure; Detection; Deterioration; Development; Diagnosis; Diagnostic; Electronic Health Record; Emergency Department Physician; Emergency Department patient; Emergency Medicine; Emergency department screening; Evaluation; Exhibits; Expert Systems; Fever; Functional disorder; Goals; Heterogeneity; High Prevalence; Hospitals; Immune response; Immunocompromised Host; Infection; Inflammation; Institution; Intervention; Knowledge; Laboratories; Learning; Liquid substance; Logic; Machine Learning; Marketing; Measures; Medical center; Modeling; Natural Language Processing; Organ; Outcome; Patients; Performance; Phase; Phenotype; Physicians; Physiological; Population; Prediction of Response to Therapy; Predictive Value; Qualifying; Reproducibility; Research Institute; Resuscitation; Risk; Role; Sample Size; Screening procedure; Semantics; Sensitivity and Specificity; Sepsis; Severities; Shock; Site; Small Business Technology Transfer Research; Specificity; Sterility; Stratification; Structure; Symptoms; Technology; Text; Therapeutic; Time; Translations; Treatment outcome; Undifferentiated; Universities; Validation; Virus Diseases; Work; biomarker panel; clinical predictors; cohort; commercialization; concept mapping; diagnostic criteria; electronic health record system; improved; improved outcome; knowledge graph; learning progression; meetings; mortality; novel; novel marker; pediatric emergency; pediatric patients; pediatric sepsis; personalized management; personalized medicine; personalized therapeutic; precision medicine; prognostic; prognostic model; prognostic performance; risk stratification; screening; septic patients; tool; treatment response; user-friendly

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. .

项目总结 这项拟议的STTR工作的总体目标是专注于商业化的 基于生物标记物增强的人工智能(AI)的儿科脓毒症筛查工具(PSCT)，可以 纳入急诊科工作流程，以加强及早识别和启动及时、 积极的个性化脓毒症治疗。 脓毒症的早期识别和及时个人化处理仍然是 儿科急救医学。早期ED对已确诊或即将发生的危重脓毒症的识别受阻 常见发热感染患病率高，鉴别特征特异性差，儿童能力差 补偿，直到休克的晚期，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，住进ICU的700万脓毒症患者中有47%的人培养为阴性。 需要改进的诊断方法来区分无菌炎症、病毒感染和细菌 疑似败血症患者的感染情况。虽然有用，但常用的基于实验室的诊断方法，如 白细胞、C反应蛋白、PCT、乳酸在小儿脓毒症治疗中的应用价值新型生物标志物面板 对于儿科脓毒症，生物标记物风险模型(PERSIVE)已被证明在预测 免疫功能低下患者的病情恶化和死亡率。持之以恒的生物标记物在 更多可能患有脓毒症的未分化儿童群体，其目标是识别约 恶化的原因仍不得而知。 败血症患者，尤其是危重患者，是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失调反应，加上 表型，强调了需要一种精确的医学PSCT方法来识别最 可能受益于有针对性的干预措施，如限制性液体复苏，早期血管活性治疗 是启动的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易碎的、嵌入在大型EHR中的模块 表现出较差特异性和阳性预测值的系统忽略自由文本中可用的重要证据 注意事项，并且不反映急诊内科专家在启动脓毒症护理时所使用的决策标准。我们 我认为对利用广泛可用的持续学习的商业PSCT有很大的需求 EHR接口标准，以提供专家知识、生物标记物、NLP和 机器学习增强早期儿科脓毒症识别和检测可预测治疗的表型 对个性化医疗的反应/结果。 。