Predicting the Absence of Serious Bacterial Infection in the PICU

预测 PICU 中不存在严重细菌感染

• 批准号: 10806039
• 负责人: Blake Martin
• 金额: $ 16.28万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10806039
• 关键词: Acceleration; Acute Renal Failure with Renal Papillary Necrosis; Admission activity; Adolescent; Adverse effects; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Automobile Driving; Award; Bacterial Infections; Biological; Biometry; Calibration; Child; Childhood; Childhood Injury; Clinical; Clinical Data; Clinical Decision Support Systems; Critical Illness; Critically ill children; Data; Data Set; Decision Making; Development; Education; Electronic Health Record; Equity; Ethics; Evaluation; Exclusion; Functional disorder; Funding; Goals; Harm Reduction; High Prevalence; Hospitals; Hour; Infection; Institution; Interview; Knowledge; Laboratories; Left; Length of Stay; Life; Machine Learning; Mentors; Methods; Mission; Modeling; Multicenter Trials; National Institute of Child Health and Human Development; Organ; Outcome; Patient-Focused Outcomes; Patients; Pattern; Pediatric Hospitals; Pediatric Intensive Care Units; Performance; Predictive Value; Probability; Provider; Pseudomembranous Colitis; Public Health; Qualifying; Research; Research Design; Research Personnel; Retrospective cohort; Risk; Sepsis; Site; System; Technical Expertise; Testing; Time; Transcript; Trust; United States National Institutes of Health; Work; career development; clinical decision support; cohort; computer human interaction; design; disability; experience; improved; improved outcome; innovation; machine learning method; machine learning model; machine learning prediction; model design; mortality; multidisciplinary; novel; pediatric sepsis; predictive modeling; predictive tools; prospective; severe injury; skills; support tools; tool; tool development; treatment duration; user centered design

Proposal Summary There are no validated systems for identifying children without serious bacterial infection (SBI) upon admission to a pediatric ICU (PICU). Given the high prevalence of SBI among critically ill children (up to 46%) and risks associated with delayed antibiotic administration, nearly 50% of children without SBI receive antibiotics while microbiologic studies are pending. However, antibiotics can have adverse effects including acute kidney injury, clostridium difficile colitis, and development of antibiotic resistance. The long-term goal of this research is to validate and disseminate machine learning (ML)-based clinical decision support (CDS) tools able to improve PICU antibiotic decision-making thereby reducing antibiotic associated harm among critically ill children. In prior work, Dr. Martin developed ML-based predictive models, which use electronic health record (EHR) inputs (vital sign, laboratory, and other clinical data), to accurately identify children without SBI upon PICU admission in a single center retrospective cohort. The central hypothesis is that these models will demonstrate similar robust performance during prospective and multicenter evaluations, and that an antibiotic decisional needs analysis of PICU clinicians will inform the optimal design of model-based CDS tools. The central hypothesis will be tested via three aims: 1) prospectively evaluate two SBI predictive models within a single center EHR and determine the potential effect on antibiotic-days per child; 2) evaluate ML model generalizability by testing them in a multicenter EHR cohort; and 3) perform a multicenter, multidisciplinary antibiotic decisional needs analysis of PICU clinicians to facilitate user-centered design of equitable model-based CDS tools. In Aim 1, two SBI predictive models will be prospectively evaluated in silent fashion (predictions not revealed to clinicians) at a single center over two years. Model predictions will be compared to patient SBI outcomes to determine their negative predictive value and potential to reduce unnecessary antibiotics. In Aim 2, the same models will be applied to a retrospective dataset of six US children's hospital PICUs (~178,000 encounters over 8+ years) to assess generalizability by determining each model's negative predictive value and potential to reduce unnecessary antibiotics. In Aim 3, a rigorous qualitative content analysis of PICU clinician interviews from five institutions will identify the values driving antibiotic decision-making and enable user-centered design of model- based CDS tools. The research is innovative because it involves development of the first clinically validated system for excluding SBI at PICU admission and uses a ML approach to do so. The research is significant as it accelerates development of generalizable antibiotic decision-making tools to assist PICU clinicians in safely minimizing unnecessary antibiotics and associated harm. The educational component of this application will allow Dr. Martin to attain expertise in biostatistics, probability, ML bias, and study design, as well as technical skills in programming, ML, and CDS. This will allow him to transition to independence and make him uniquely qualified to develop, validate, and implement CDS tools able to improve the outcomes of critically ill children.

提案摘要 入院时没有经过验证的系统来识别没有严重细菌感染（SBI）的儿童 到儿科ICU（PICU）。鉴于患病儿童中SBI的高龄（最多46％），风险 与延迟的抗生素给药有关，将近50％的没有SBI的儿童接受抗生素，而 微生物学研究正在等待。但是，抗生素可能会产生不良反应，包括急性肾脏损伤， 艰难梭菌结肠炎和抗生素耐药性的发展。这项研究的长期目标是 验证和传播机器学习（ML）的临床决策支持（CD）工具 PICU抗生素决策，从而减少了重症儿童抗生素相关的危害。在 先前的工作，马丁博士开发了基于ML的预测模型，该模型使用电子健康记录（EHR）输入 （生命体征，实验室和其他临床数据），以准确识别PICU入院时没有SBI的儿童 在单个中心回顾性队列中。中心假设是这些模型将表现出相似的 在预期和多中心评估期间的良好表现，并且是抗生素的决策需求 PICU临床医生的分析将为基于模型的CDS工具的最佳设计提供信息。中心假设将 通过三个目标进行测试：1）在单个中心EHR中前瞻性评估两个SBI预测模型，并且 确定对每个儿童抗生素日的潜在影响； 2）通过测试评估ML模型的推广性 他们在多中心EHR队列中； 3）执行多中心，多学科的抗生素决定性需求 对PICU临床医生的分析，以促进基于公平模型的CDS工具以用户为中心的设计。在AIM 1中，两个 SBI预测模型将以无声的方式进行预期评估（未向临床医生揭示的预测） 一个单一的中心两年。将模型预测与患者SBI结果进行比较以确定其 负预测价值和减少不必要的抗生素的潜力。在AIM 2中，相同的模型将是 适用于六个美国儿童医院Picus的回顾性数据集（在8年以上的〜178,000次遭遇）到 通过确定每个模型的负预测价值和减少潜力来评估普遍性 不必要的抗生素。在AIM 3中，对PICU临床医生的访谈进行了严格的定性内容分析 机构将确定驱动抗生素决策的价值，并使以用户为中心的模型设计 - 基于CDS工具。该研究具有创新性，因为它涉及开发第一个经过临床验证的 用于排除PICU入院的SBI的系统，并使用ML方法进行此操作。这项研究很重要 加速开发可推广的抗生素决策工具，以帮助PICU临床医生安全地 最大程度地减少不必要的抗生素和相关的伤害。该应用程序的教育部分将 允许Martin博士获得生物统计学，概率，ML偏见和研究设计方面的专业知识以及技术 编程，ML和CD的技能。这将使他能够过渡到独立并使他独特 有资格开发，验证和实施能够改善重症儿童结果的CD工具。