A Real-Time Computational System for Detecting ARDS Using Ventilator Waveform Data

使用呼吸机波形数据检测 ARDS 的实时计算系统

• 批准号: 9980981
• 负责人: Gregory Boyd Rehm
• 金额: $ 1.54万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980981
• 关键词: Acute; Acute respiratory failure; Address; Adult Respiratory Distress Syndrome; Affect; Airway Resistance; Algorithms; American; Automation; Berlin; Chest; Classification; Clinical; Complex; Computational algorithm; Contracts; Critical Illness; Data; Data Analyses; Data Set; Decision Support Model; Detection; Deterioration; Development; Diagnosis; Diagnostic; Diagnostic tests; Discipline; Disease; Electronic Health Record; Hospital Mortality; Hour; Hypoxemia; Intensive Care Units; Intervention; Learning; Life; Lung; Machine Learning; Mechanical ventilation; Medical History; Modeling; Outcome; Patients; Pattern; Performance; Phase; Physicians; Physiological; Physiology; Provider; Recording of previous events; Reporting; Reproducibility; Research; Respiratory Failure; Sampling; Savings; Scanning; Series; Severities; Specific qualifier value; Statistical Methods; System; Techniques; Technology; Time; United States; Ventilator; Work; adjudicate; advanced analytics; base; clinical decision support; clinical predictors; clinical translation; cohort; computer science; deep learning; high dimensionality; high risk; improved; improved outcome; innovation; insight; machine learning algorithm; markov model; model development; mortality; novel; predictive modeling; predictive tools; prevent; prognostic; recurrent neural network; respiratory; success; targeted treatment

Project Summary: Acute respiratory distress syndrome (ARDS) is a severe form of acute hypoxemic respiratory failure affecting 10% of patients admitted to the intensive care unit (ICU) in the United States. In-hospital mortality of 35-46% has been reported across the spectrum of mild-severe ARDS, and one third of patients with initially mild ARDS will progress to moderate or severe ARDS. Over the last 20 years, multiple studies have reported improved outcomes for ARDS patients using specific ARDS targeted therapies. However, ARDS remains persistently under-recognized and challenging to diagnose. Only one third of ICU providers correctly identify ARDS on the first day when diagnostic criteria are met, and less than two thirds ever recognize the diagnosis in the ICU. This under recognition of ARDS may prevent some patients from receiving lifesaving therapies necessary for treating the disease. Attempts to automate ARDS diagnosis using rule- based algorithms have seen limited success, and require analysis of subjective data from patient histories, like chest scans, which limit diagnosis automation, timeliness, and study reproducibility. To improve the current state of the art of ARDS detection technology, we intend to utilize objective and readily available data including both ventilator waveform data, (VWD) and electronic health record (EMR) data to 1) improve the recognition of ARDS, and 2) identify high-risk ARDS patients most likely to benefit from additional ARDS treatments. For this task, we will make use of an existing dataset of VWD from over 500 patients receiving mechanical ventilation, including 156 patients with confirmed ARDS. Our preliminary analyses using a machine learned model and a subset of lung physiology features derived solely from VWD, suggest that ARDS can be diagnosed in the absence of a chest scan or medical history. In Aim 1 of this proposal, we will improve our existing model used for discriminating ARDS by adding objective EMR data, and additional features extracted from VWD, such as patient respiratory compliance and airway resistance. Our next focus will be to predict worsening of ARDS severity in intubated patients based on Berlin criteria. So in Aim 2, we will evaluate the best tools for predicting increases in ARDS severity, and which types of temporal information yield the best predictive results. We hypothesize that model development using additional objective data derived from VWD analysis and the EMR, along with advanced analytic techniques, will further improve ARDS diagnosis, and enable the prediction of clinical trajectories in patients with ARDS. The proposed work will yield innovative clinical decision support models that can be used to improve the state of the art in automated ARDS diagnosis. Our predictive modeling will also enable greater insight into the times when physicians can perform clinical interventions to arrest ARDS induced physiologic deterioration. Ultimately, these innovations could save lives by quickly detecting ARDS, and alerting physicians to begin or intensify ARDS focused therapies based on patient pathophysiologic state.

项目总结： 急性呼吸窘迫综合征(ARDS)是一种严重的急性低氧性呼吸衰竭，影响 10%的患者住进了美国的重症监护病房(ICU)。住院死亡率为35%-46% 轻度-重度ARDS的报道，最初轻度ARDS的患者中有三分之一将进展到 中度或重度ARDS。在过去的20年里，多项研究报告了ARDS患者预后的改善。 使用特定的ARDS靶向治疗。然而，ARDS仍然长期得不到认可，并具有挑战性 诊断。只有三分之一的ICU提供者在符合诊断标准的第一天就正确地识别了ARDS，并且 只有不到三分之二的人曾经在ICU承认过诊断。在认识到ARDS的情况下，这可能会阻止一些患者 接受治疗疾病所需的挽救生命的疗法。尝试使用规则自动诊断ARDS- 基于算法的成功有限，需要分析患者病史的主观数据，如胸部扫描， 这限制了诊断的自动化、及时性和研究的重复性。 为了改善ARDS检测技术的现状，我们打算客观和方便地利用 可用的数据包括呼吸机波形数据(VWD)和电子健康记录(EMR)数据，以1)改善 识别ARDS，以及2)确定最有可能从额外ARDS治疗中受益的高危ARDS患者。 在这项任务中，我们将利用现有的VWD数据集，这些数据集来自500多名接受机械通气的患者， 包括156名确诊为ARDS的患者。我们的初步分析使用了机器学习模型和 仅来自VWD的肺生理学特征表明，ARDS可以在没有胸部扫描或 病史。在本提案的目标1中，我们将改进现有用于区分ARDS的模型，方法是添加 客观的EMR数据，以及从VWD中提取的其他特征，如患者的呼吸顺应性和呼吸道 抵抗。我们的下一个重点将是根据柏林标准预测插管患者ARDS严重程度的恶化。所以 在目标2中，我们将评估预测ARDS严重性增加的最佳工具，以及哪些类型的临床期 信息产生最好的预测结果。 我们假设，使用来自VWD分析的额外客观数据和 电子病历结合先进的分析技术，将进一步改善ARDS的诊断，并使预测 急性呼吸窘迫综合征患者的临床轨迹。拟议的工作将产生创新的临床决策支持模型 可用于提高ARDS自动诊断的技术水平。我们的预测建模还将实现更大的 洞察医生可以进行临床干预以阻止ARDS引起的生理性疾病的时间 恶化。最终，这些创新可以通过快速检测ARDS并提醒医生开始治疗来拯救生命 或根据患者的病理生理状态加强ARDS的重点治疗。