Predicting earliest safe extubation time in pediatric patients

项目摘要 决定何时拔管的病人在儿科重症监护病房（PICU）是一个挑战，临床医生面临的 每一天.缺乏儿科拔管的共识指南，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 转化为增加的发病率，死亡率和成本，所产生的不必要的呼吸机天， 延迟拔管和拔管失败后重新插管。该项目的长期目标是利用 人工智能的力量，以优化识别拔管准备在PICU。客观 该提案的一个主要目的是使用大型电子健康记录（EHR）创建机器学习模型。 预测何时拔管患者并估计可以节省多少呼吸机天数的数据集 如果这种模式在实践中得到应用。在EHR中部署此类模型作为实时决策支持 工具可以通过减少护理差异和识别患者子集来安全地缩短拔管时间， 提前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弗朗西斯科建立模型，以估计拔管准备PICU患者（目的 1）。研究人员将应用人为因素设计原则，旨在提高工具的有效性， 帮助人类以更高的可靠性完成工作，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整合到我们的模型中（目标2）。的 模型的性能将用标准指标进行评估，以及对 节省的呼吸机天数，反映了潜在的健康影响（目标3）。这个项目将提前拔管 为重症儿童的实践，产生一个预测工具，准备在EHR中进行前瞻性测试， 为呼吸衰竭患者提供高可靠性的医疗保健。这项研究将推动 NHLBI的使命是利用数据科学改善肺部疾病患者的治疗。拟议 由专家指导团队指导的培训将丰富申请人的数据知识和技能 科学、机器学习和预测以及临床信息学。内容专业知识，研究 能力，并在定量方法的培训，申请人将收到将准备她很好地提高 科学知识和临床实践在她的职业生涯作为一个独立的研究人员。

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.