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 没有经过验证的系统来识别入院时没有严重细菌感染（SBI）的儿童 儿科ICU（PICU）鉴于SBI在重症儿童中的高患病率（高达46%）和风险 与抗生素给药延迟相关，近50%的无SBI儿童接受抗生素治疗， 微生物研究正在进行中。然而，抗生素可能有副作用，包括急性肾损伤， 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 验证和传播基于机器学习（ML）的临床决策支持（CDS）工具， PICU抗生素决策，从而减少重症儿童中的抗生素相关危害。在 在之前的工作中，Martin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入院时无SBI的儿童 单中心回顾性队列研究。核心假设是，这些模型将证明类似的 在前瞻性和多中心评价期间的稳健性能，以及抗生素决策需要 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。核心假设将 通过三个目标进行测试：1）前瞻性地评估单个中心EHR中的两个SBI预测模型， 确定对每个儿童住院天数的潜在影响; 2）通过测试评估ML模型的泛化能力 他们在多中心EHR队列中; 3）执行多中心，多学科抗生素决策需求 PICU临床医生的分析，以促进公平的基于模型的CDS工具的以用户为中心的设计。在目标1中， SBI预测模型将以沉默的方式进行前瞻性评价（预测结果不向临床医生透露）， 一个中心超过两年。将模型预测与患者SBI结果进行比较，以确定其 阴性预测值和减少不必要抗生素的潜力。在目标2中，相同的模型将被 应用于6家美国儿童医院PICU的回顾性数据集（8年以上约178，000次就诊）， 通过确定每个模型的阴性预测值和降低 不必要的抗生素在目标3中，对来自五个国家的PICU临床医生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观，并实现以用户为中心的模型设计， CDS工具。这项研究是创新的，因为它涉及到第一个临床验证的发展， 该系统用于在PICU入院时排除SBI，并使用ML方法来这样做。这项研究意义重大，因为它 加速开发可推广的抗生素决策工具，以帮助PICU临床医生安全地使用抗生素。 尽量减少不必要的抗生素和相关的伤害。此应用程序的教育组件将 允许马丁博士获得生物统计学，概率，ML偏倚和研究设计以及技术方面的专业知识 编程，ML和CDS技能。这将使他过渡到独立，使他独特的 有资格开发，验证和实施CDS工具，能够改善重症儿童的结果。