ERI: Precision Dosing in Critical Care: An Automated Modeling and Control Approach

ERI：重症监护中的精确剂量：自动建模和控制方法

• 批准号: 2138929
• 负责人: Hossein Mirinejad
• 金额: $ 20万
• 依托单位: Kent State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138929&HistoricalAwards=false
• 关键词: ERI; Precision; Dosing; Critical; Care

This Engineering Research Initiation (ERI) grant will fund research that enables patient-specific fluid resuscitation therapies in critical care of hemorrhage, a leading cause of deaths from traumatic injuries, thereby promoting the progress of science and advancing the national health and welfare. Death caused by hemorrhagic injuries may be preventable if critical care interventions are urgently and effectively deployed to restore lost blood volume. Fluid infusion dosages, including both rate and timing of administration, notably impact resuscitation outcomes. Under-dosing strategies are inefficient in restoring cardiac functions, whereas overly aggressive dosing regimens may lead to serious adverse events such as soft clots, thereby increasing mortality rates. Most existing dosing techniques are based on one-size-fits-all models or a small number of dose-response profiles. Such approaches sacrifice the control performance at the expense of robustness against inter-patient variability. A framework for identifying the correct dosage for each individual patient is currently lacking. This project will address this deficiency by developing a data-driven, integrated modeling and control framework for precision dosing that is able to resolve individual differences in dose response and predict optimal patient-specific dosing strategies, even with limited and noisy measurements. The knowledge advanced by this project may not only improve patient outcomes, but also significantly reduce costs associated with critical care services. Efforts aiming to attract and train students in STEM will include K-12 outreach using a fluid resuscitation testbed, as well as research experiences for undergraduate students.This research aims to make fundamental contributions to the science of physiological closed-loop control systems: complex cyber-physical systems that involve interactions between patient monitors, therapeutic devices, complex patient physiology, and clinical users. It will achieve this outcome by first developing and validating a new system identification framework that uses a statistical bound on the prediction error to constrain a robust nonlinear state space model of a patient-specific dose response. Next, it will formulate an automated, computationally efficient control strategy for achieving the desired outcome subject to the predicted dose-response dynamics, initial and boundary conditions, and mixed state-control path constraints. Finally, feasibility and performance will be assessed against real-world clinical datasets of human subject dose-response measurements. Evaluation will be conducted both using computer simulations and by implementing computational models of physiologic variables and computer-based fluid resuscitation controllers in a hardware-in-the-loop testing platform.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项工程研究启动（ERI）赠款将资助研究，使患者特定的液体复苏疗法在重症监护出血，创伤性损伤死亡的主要原因，从而促进科学的进步和推进国家健康和福利。如果紧急有效地部署重症监护干预措施以恢复失血量，则出血性损伤引起的死亡可能是可以预防的。液体输注剂量，包括给药速率和时间，显著影响复苏结果。剂量不足的策略在恢复心脏功能方面效率低下，而过度积极的给药方案可能导致严重的不良事件，如软凝块，从而增加死亡率。大多数现有的给药技术是基于一种尺寸适合所有模型或少量的剂量-反应曲线。这种方法牺牲了控制性能，代价是对患者间变异性的鲁棒性。目前缺乏一个框架来确定每个患者的正确剂量。该项目将通过开发一个数据驱动的、集成的建模和控制框架来解决这一缺陷，该框架能够解决剂量反应的个体差异，并预测最佳的患者特定给药策略，即使是有限的和有噪声的测量。该项目所提供的知识不仅可以改善患者的治疗效果，还可以显著降低与重症监护服务相关的成本。旨在吸引和培养STEM学生的努力将包括使用液体复苏试验台的K-12推广活动，以及为本科生提供研究经验。该研究旨在为生理闭环控制系统科学做出基础性贡献：涉及患者监护仪，治疗设备，复杂患者生理学和临床用户之间交互的复杂网络物理系统。它将通过首先开发和验证一个新的系统识别框架来实现这一结果，该框架使用预测误差的统计界限来约束患者特定剂量反应的鲁棒非线性状态空间模型。接下来，它将制定一个自动化的，计算效率高的控制策略，以实现预期的结果受到预测的剂量反应动力学，初始和边界条件，以及混合状态控制路径约束。最后，将根据人类受试者剂量反应测量的真实临床数据集评估可行性和性能。评估将使用计算机模拟和实现生理变量的计算模型和基于计算机的液体复苏控制器在硬件在环测试平台进行。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care

重症监护中自动输液的无模型强化学习

• 发表时间: 2022
• 期刊: IEEEEMBS International Conference on Biomedical and Health Informatics BHI
• 作者: Estiri, Elham;Mirinejad, Hossein
• 通讯作者: Mirinejad, Hossein

Precision Dosing in Critical Care: Application of Machine Learning in Fluid Therapy

重症监护中的精准剂量：机器学习在液体治疗中的应用

• 发表时间: 2023
• 期刊: IEEE International Conference on Digital Health (ICDH 2023
• 作者: Estiri, Elham;Mirinejad, Hossein
• 通讯作者: Mirinejad, Hossein