Automated Computational Modeling and Adaptive Control for Critical Patient Resuscitation

危重病人复苏的自动计算建模和自适应控制

• 批准号: 1437532
• 负责人: Karolos Grigoriadis
• 金额: $ 32万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1437532&HistoricalAwards=false
• 关键词: Automated; Computational; Modeling; Adaptive; Control

Physicians face enormous challenges in making decisions to resuscitate and stabilize critically ill and injured patients based on rapidly changing vital signs. This project will investigate automated processing of physiological measurements, using high-speed computational models to provide near-instantaneous treatment recommendations to stabilize blood pressure, cardiac output and renal function. Additionally, feedback control algorithms will be developed capable of automatically regulating the administration of drugs and fluids to optimize critical care, subject to the specific physiological state of the patient. This advance in continually monitored and adjusted personalized treatment is expected to benefit patients suffering from trauma, burn, infection, and shock. The resulting decision assistance system and adaptive closed-loop drug and fluid delivery system will greatly benefit the accuracy and reliability of critical care treatments, resulting in increased survival rates and improved therapeutic outcomes. The project will develop adaptive models of cardio-vascular and fluid response, subject to the injection of vasoactive drugs and fluid therapy. The corresponding drug and fluid administration problem is challenged by a changing physiological dynamic response and a significant time-delay in the response due to drug absorption. Multi-model observers will be investigated to provide instantaneous dosage recommendations to doctors to achieve targeted values of blood pressure, cardiac output and urinary output. The models will compute the patient's responsiveness to various drugs and fluids, and will self-adapt to varying responses to treatment from patient-to-patient and within a single patient over time (intra-patient and inter-patient variability). Detection algorithms will be developed to identify potential sudden changes in the patient's physiological response, such as the presence of an internal hemorrhage, and alert the doctors. Additionally, model-based adaptive and robust closed-loop drug infusion algorithms will be developed to automate the drug administration process for optimized patient resuscitation. The research team will collaborate with medical experts that will provide physiological data from animal experiments and will assist in the evaluation of the developed models and decision support algorithms.

医生在决定基于迅速变化的生命体征来复苏和稳定危重的患者的决策方面面临巨大挑战。该项目将使用高速计算模型来研究生理测量的自动处理，以提供近乎传真的治疗建议，以稳定血压，心输出量和肾功能。此外，将开发反馈控制算法能够自动调节药物和液体的给药以优化重症监护，但要遵守患者的特定生理状态。不断监测和调整的个性化治疗中的这一进展有望使患有创伤，烧伤，感染和震惊的患者受益。由此产生的决策援助系统和自适应闭环药物和流体输送系统将极大地有益于重症监护治疗的准确性和可靠性，从而提高存活率和改善的治疗结果。该项目将在注射血管活性药物和液体疗法的情况下，开发心血管和液体反应的自适应模型。相应的药物和流体给药问题受到生理动态反应的变化和由于吸收药物的反应而发生的明显时间延迟的挑战。将研究多模型观察者，以向医生提供瞬时剂量建议，以实现血压，心脏输出和尿量的目标值。这些模型将计算患者对各种药物和液体的反应性，并会自适应从患者到患者以及随着时间的时间（患者内部和患者的患者）（患者内和患者间的变异性）的不同反应。将开发检测算法，以确定患者生理反应的潜在突然变化，例如内部出血的存在，并提醒医生。此外，将开发基于模型的自适应和健壮的闭环药物输注算法，以使药物管理过程自动化以优化患者复苏。研究团队将与医学专家合作，该专家将提供来自动物实验的生理数据，并将有助于评估开发的模型和决策支持算法。