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RAPID: Data-Driven Models to Optimize Ventilator Therapy in ICU COVID Patients

RAPID：数据驱动模型优化 ICU 新冠患者的呼吸机治疗

基本信息

批准号：
2031195
负责人：
Sridevi Sarma
金额：
$ 20万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-15 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031195&HistoricalAwards=false
关键词：
RAPID Data Driven Models Optimize

项目摘要

The novel Coronavirus (COVID-19) is one of four infectious diseases caused by the SARS-CoV-2 virus. Although the clinical signs and patient symptoms of this complicated disease vary in presentation and severity, clinicians and investigators have reported constitutional symptoms (cough and fever), upper and lower respiratory tract symptoms, as well as gastrointestinal symptoms. Among the most concerning is the life threatening acute respiratory distress syndrome (ARDS) in patients. The pathophysiology of severe ARDS results from a rapid decline in pulmonary function and requires intubation of patients in critical condition for invasive mechanical ventilation to combat lung recruitability, reduced peripheral capillary oxygen saturation (SpO2) and risks of organ failure and death. Ventilator settings to increase SpO2 and oxygen delivery is achieved with positive end-expiratory pressure (PEEP). However, controlling ventilation at a high PEEP for extended periods of time significantly increases risk for ventilator-associated lung injury (VALI). This RAPID project will develop novel engineering strategies for optimal ventilator control to maximize SpO2 in minimal time, while minimizing PEEP and the duration of ventilator use are needed to minimize VALI and subsequent complications, and to improve favorable patient outcomes. In the management of patients with COVID-19, these strategies are significant to optimize oxygen delivery, minimal invasive ventilator use and mechanical lung injury. Further, the understanding of ventilator requirements and operative settings highlights the need for available ventilators. The management of severe ARDS is complicated and strategies and protocols are desperately needed.To achieve this goal, we will develop data-driven linear parameter-varying (LPV) dynamical systems models that relate patient clinical state and ventilator inputs to the output variable patient SpO2. Patient state will be characterized using data from the electronic health record (EHR) and minute-by-minute physiological time-series (PTS) data (e.g., heart rate, respiratory rate, SpO2) acquired from patient monitoring. We will first develop the LPV model using retrospective data from non-COVID-19 patients who are on ventilators to help treat conditions such as pneumonia and ARDS. Then, we will test the predictive capabilities of the LPV model in COVID-19 patients who are placed on ventilators. Finally, we will develop an optimal ventilator control strategy for COVID-19 patients to regulate SpO2 levels in ICU patients based on the LPV model. Attempting to control a complex biological system using control strategies based on mechanistic models is generally intractable. However, the LPV framework allows for sophisticated optimal strategies to be implemented that not only allow for better performance than other classical methods, but also provides stability and performance guarantees.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.

新型冠状病毒（COVID-19）是由SARS-CoV-2病毒引起的四种传染病之一。尽管这种复杂疾病的临床体征和患者症状在表现和严重程度上各不相同，但临床医生和研究人员报告了全身症状（咳嗽和发热）、上呼吸道和下呼吸道症状以及胃肠道症状。其中最令人关注的是危及患者生命的急性呼吸窘迫综合征（ARDS）。严重ARDS的病理生理学是由于肺功能快速下降，需要对危重患者进行插管，进行有创机械通气，以对抗肺复张性、外周毛细血管氧饱和度（SpO 2）降低以及器官衰竭和死亡风险。通过呼气末正压（PEEP）实现增加SpO 2和氧气输送的呼吸机设置。然而，长时间在高PEEP下控制通气会显著增加呼吸机相关性肺损伤（VALI）的风险。该RAPID项目将开发新型工程策略，用于优化呼吸机控制，以在最短时间内最大限度地提高SpO 2，同时最大限度地减少PEEP和呼吸机使用持续时间，以最大限度地减少VALI和后续并发症，并改善有利的患者结局。在COVID-19患者的管理中，这些策略对于优化氧气输送、微创呼吸机使用和机械性肺损伤具有重要意义。此外，对呼吸机要求和手术设置的理解强调了对可用呼吸机的需求。严重ARDS的管理是复杂的，策略和协议是迫切需要的。为了实现这一目标，我们将开发数据驱动的线性参数变化（LPV）动力系统模型，将患者的临床状态和呼吸机输入到输出变量患者SpO 2。将使用来自电子健康记录（EHR）的数据和逐分钟生理时间序列（PTS）数据（例如，心率、呼吸率、SpO 2）。我们将首先使用非COVID-19患者的回顾性数据开发LPV模型，这些患者使用呼吸机帮助治疗肺炎和ARDS等疾病。然后，我们将测试LPV模型在使用呼吸机的COVID-19患者中的预测能力。最后，我们将根据LPV模型为COVID-19患者制定最佳呼吸机控制策略，以调节ICU患者的SpO 2水平。试图使用基于机械模型的控制策略来控制复杂的生物系统通常是棘手的。然而，LPV框架允许实施复杂的优化策略，不仅比其他经典方法具有更好的性能，而且还提供稳定性和性能保证。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。