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Novel Deep Learning Tools for Clinical Decision Support in Postoperative Pain Management

用于术后疼痛管理临床决策支持的新型深度学习工具

基本信息

批准号：
10684876
负责人：
Baiming Zou
金额：
$ 40.91万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-16 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10684876
关键词：
Acute Address Age Algorithms American Analgesics Anesthesia procedures Basic Science Caring Chronic Clinical Cohort Studies Complement Complex Critical Care Data Data Analyses Data Sources Databases Disparity Education Electronic Health Record Etiology Florida Funding Goals Health Healthcare Systems Heterogeneity Image Individual Inpatients Intervention Investigation Length of Stay Measures Mediation Medical Care Costs Methods Modeling Morbidity - disease rate Network-based Neural Network Simulation North Carolina Operative Surgical Procedures Outcome Pain Pain Measurement Pain intensity Pain management Patients Perioperative Pharmaceutical Preparations Physicians Policies Policy Maker Population Postoperative Pain Postoperative Period Process Race Randomized, Controlled Trials Regimen Relaxation Research Research Personnel Risk Risk Factors Socioeconomic Status Structure Subgroup Techniques Testing Time United States National Institutes of Health Universities analytical tool circadian clinical care clinical decision support clinical practice cohort computerized tools convolutional neural network cost cost effective data warehouse deep learning deep learning model deep neural network design experience improved individual patient learning strategy machine learning model mortality novel opioid overuse outcome prediction pain outcome pain score patient subsets personalized medicine response semiparametric sex substance use tool user friendly software

项目摘要

Abstract Postoperative pain (POP) burdens millions of Americans, and it costs hundreds of billions dollars to the US healthcare system annually. Poorly managed acute POP often leads to increased morbidity, mortality, and many other complications, such as chronic POP and opioid overuse. Accurate prediction of POP outcomes and in-depth understandings of causal mechanisms of POP is critical to develop effective POP management. Also, many POP studies indicate heterogeneity of responses to anesthesia methods and postoperative substance use, suggesting a critical need for effective methods to accurately identify patient subgroups for more effective POP management tailored to the individual patient's needs. However, achieving these goals is challenging due to the complex POP mechanisms and limited data from ideal large randomized controlled trials. On the other hand, abundant observational POP data found in surgery patients' electronic health records (EHRs) are readily available, and they can serve as a cost-effective alternative to address the critical challenges in POP management. However, the etiology of POP is intricate, i.e. many factors may interweave with each other and impact POP outcomes non-linearly and non-additively, introducing daunting modeling challenges. Furthermore, confounding, a major concern associated with observational data, represents a particular challenge for conducting causal analysis on POP data. Also, POP outcomes such as POP intensity scores are often irregularly and repeatedly measured, and distributed non-normally with two distinct data processes, requiring more advanced analysis methods. This proposal aims to overcome these analytic and modeling challenges with state-of-the-art deep learning methods to improve POP management. Speciﬁcally, we will 1) establish robust deep learning models for more accurate predictions of both acute and chronic POP to achieve timely POP control and care; 2) develop valid deep learning based semi-parametric methods to identify true causal factors and mechanisms of POP to design more effective POP management interventions; and 3) build powerful models to conduct robust hidden subgroup analysis to develop the optimal POP management tailored to the individual patient's needs. Methods developed in Aims 1 3 are motivated and will be tested by two unique data: a large EHR data from the University of North Carolina at Chapel Hill's Carolina Data Warehouse for Health (CDW-H), and a high-quality cohort data from NIH- funded TEMporal PostOperative Pain Signatures study, which complements the CDW-H in scale and scope. The project will elucidate the scientiﬁc underpinnings of POP mechanisms and provide improved POP management.

抽象的术后疼痛（POP）给数百万美国人带来负担，并给美国造成数千亿美元的损失每年的医疗保健系统。急性 POP 管理不善常常导致发病率、死亡率增加，并导致许多人死亡。其他并发症，例如慢性 POP 和阿片类药物过度使用。 POP结果的准确预测和深入了解 POP 的因果机制对于制定有效的 POP 管理至关重要。还有很多POP 研究表明对麻醉方法和术后物质使用的反应存在异质性，表明迫切需要有效的方法来准确识别患者亚组，以实现更有效的 POP 管理根据患者的个体需求量身定制。然而，由于复杂的情况，实现这些目标具有挑战性 POP 机制和来自理想大型随机对照试验的有限数据。另一方面，丰富手术患者电子健康记录 (EHR) 中的观察性 POP 数据随时可用，并且它们可以作为解决持久性有机污染物管理中的关键挑战的具有成本效益的替代方案。然而，POP的病因复杂，多种因素可能相互交织影响POP的发生。结果是非线性和非相加的，带来了令人畏惧的建模挑战。此外，令人困惑的是，与观测数据相关的一个主要问题是进行因果分析的一个特殊挑战 POP 数据分析。此外，POP 结果（例如 POP 强度分数）通常是不规则且重复的使用两个不同的数据过程进行测量和非正态分布，需要更高级的分析方法。该提案旨在通过最先进的深度学习来克服这些分析和建模挑战学习改进 POP 管理的方法。具体来说，我们将1）建立强大的深度学习模型更准确地预测急性和慢性 POP，以实现及时的 POP 控制和护理； 2）制定有效的基于深度学习的半参数方法来识别 POP 的真正因果因素和机制进行设计更有效的持久性有机污染物管理干预措施； 3）建立强大的模型来进行稳健的隐藏子群分析以制定适合个体患者需求的最佳 POP 管理。开发方法目标 1 3 受到激励，将通过两个独特的数据进行测试：来自北方大学的大型 EHR 数据 Carolina 位于教堂山的 Carolina Data Warehouse for Health (CDW-H)，以及来自 NIH 的高质量队列数据- 资助了 TEMporal PostOperative Pain Signatures 研究，该研究在规模和范围上补充了 CDW-H。这该项目将阐明 POP 机制的科学基础并提供改进的 POP 管理。