Predicting earliest safe extubation time in pediatric patients

预测儿科患者最早安全拔管时间

• 批准号: 10832789
• 负责人: Jean Digitale
• 金额: $ 0.25万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10832789
• 关键词: Adult; Area; Artificial Intelligence; Breathing; California; Caring; Characteristics; Child; Child Care; Childhood; Clinical; Clinical Informatics; Clinical Research; Clinical Skills; Competence; Consensus; Critically ill children; Data; Data Science; Data Scientist; Data Set; Decision Making; Delirium; Development; Effectiveness; Electronic Health Record; Event; Face; Failure; Foundational Skills; Future; Goals; Guidelines; Health; Health Care Costs; Healthcare; Hospitalized Child; Hour; Human; Intubation; Judgment; Knowledge; Length of Stay; Light; Liquid substance; Lung diseases; Machine Learning; Mechanical ventilation; Medical; Mentors; Mentorship; Methodology; Methods; Mission; Modeling; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Occupations; Patient Care; Patients; Pediatric Intensive Care Units; Performance; Physicians; Physiological; Pneumonia; Probability; Process; Protocols documentation; ROC Curve; Readiness; Reporting; Research; Research Personnel; Resources; Respiratory Disease; Respiratory Failure; Respiratory distress; Safety; San Francisco; Specificity; Surveys; Testing; Time; Training; Translating; Tube; Universities; Variant; Ventilator; career; clinical practice; cost; critical care nursing; design; experience; high dimensionality; high risk; improved; inpatient service; lung injury; machine learning algorithm; machine learning model; model building; model development; mortality; novel; patient subsets; pediatric patients; poor health outcome; predictive modeling; predictive tools; prospective test; rate of change; skills; standardized care; statistical and machine learning; support tools; technology validation; tool; wasting

PROJECT SUMMARY Determining when to extubate patients in the pediatric intensive care unit (PICU) is a challenge clinicians face each day. Consensus guidelines for pediatric extubation are lacking and, in light of this, most pediatric studies conclude that the decision to extubate relies ultimately on clinician judgment. The resulting variation in care translates to increased morbidity, mortality, and costs that arise from both unnecessary ventilator days from delayed extubation and re-intubation from extubation failure. The long-term goal of this project is to harness the power of artificial intelligence to optimize identification of extubation readiness in the PICU. The objective of this proposal is to create machine learning models using a large electronic health record (EHR) dataset to predict when to extubate patients and to estimate how many ventilator days could be saved if such models were used in practice. Deploying such models in the EHR as a real-time decision support tool could safely shorten extubation times by decreasing variation in care and identifying subsets of patients for earlier, safe extubation. This study will use EHR data from mechanically ventilated PICU patients at the University of California, San Francisco to build models to estimate extubation readiness for PICU patients (Aim 1). The investigators will apply human factor design principles, which aim to increase usefulness of tools and help humans do their jobs with higher reliability, to improve model performance. We will use a novel method, expert-augmented machine learning, to incorporate clinician knowledge directly into our models (Aim 2). The performance of the models will be evaluated with standard metrics, as well as with an estimate of number of ventilator days saved, reflecting the potential health impact (Aim 3). This project will advance extubation practices for critically ill children, yielding a predictive tool ready for prospective testing in the EHR that moves toward delivering high reliability healthcare for patients with respiratory failure. This research will advance NHLBI's mission of using data science to improve treatment of patients with lung diseases. The proposed training, guided by an expert mentorship team, will enrich the applicant's knowledge of and skills in data science, machine learning and prediction, and clinical informatics. The content expertise, research competency, and training in quantitative methods the applicant will receive will prepare her well to improve scientific knowledge and clinical practice in her career as an independent researcher.

项目总结 决定何时给儿科重症监护病房(PICU)的患者拔管是临床医生面临的挑战 每一天。儿科拔管缺乏共识指南，有鉴于此，大多数儿科研究 结论是拔管的决定最终取决于临床医生的判断。由此产生的护理差异 转化为增加发病率、死亡率和成本，这两个不必要的呼吸机日从 拔管失败后延迟拔管和重新插管。这个项目的长期目标是利用 人工智能在优化PICU拔管准备情况识别方面的力量。目标是 这项提议的目的是使用大型电子健康记录(EHR)创建机器学习模型 用于预测患者何时拔管并估计可节省多少呼吸机天数的数据集 如果这样的模型在实践中使用。在电子病历中部署此类模型作为实时决策支持 Tool可以安全地缩短拔管时间，因为它减少了护理的差异，并确定了患者的亚群 早些时候，安全拔管。这项研究将使用来自机械通气的PICU患者的EHR数据 加州大学旧金山分校建立模型以评估PICU患者的拔管准备情况(AIM 1)。调查人员将应用人的因素设计原则，旨在增加工具和 帮助人类以更高的可靠性完成他们的工作，以提高模型性能。我们将使用一种新的方法， 专家增强的机器学习，将临床医生的知识直接纳入我们的模型(目标2)。这个 模型的性能将使用标准指标以及估计的数量进行评估 节省呼吸机天数，反映潜在的健康影响(目标3)。该项目将推进拔管。 针对危重儿童的实践，产生了一种预测工具，可用于在移动的EHR中进行预期测试 为呼吸衰竭患者提供高可靠性的医疗保健。这项研究将会取得进展 NHLBI的使命是使用数据科学来改善肺部疾病患者的治疗。建议数 在专家指导团队的指导下，培训将丰富申请人在数据方面的知识和技能 科学、机器学习和预测以及临床信息学。内容专业知识、研究 应聘者将接受的能力和量化方法的培训将为她的进步做好准备 在独立研究人员的职业生涯中，她的科学知识和临床实践。