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

项目摘要： 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， 在美国，10%的患者入住重症监护室（ICU）。住院死亡率为35-46%， 在轻-重度ARDS的范围内报告，三分之一的最初轻度ARDS患者将进展为 中度或重度ARDS。在过去的20年里，多项研究报告了ARDS患者的预后改善 使用特定的ARDS靶向治疗。然而，ARDS仍然持续被低估， 诊断。只有三分之一的ICU提供者在符合诊断标准的第一天正确识别出ARDS， 只有不到三分之二的人能在重症监护室里认出诊断结果。这在认识到ARDS可能会阻止一些患者 接受治疗疾病所必需的救命疗法。使用规则自动化ARDS诊断的尝试- 基于的算法已经取得了有限的成功，并且需要分析来自患者历史的主观数据，如胸部扫描， 这限制了诊断的自动化、及时性和研究的可重复性。 为了提高ARDS检测技术的现有水平，我们打算利用客观和容易 包括呼吸机波形数据（VWD）和电子健康记录（EMR）数据的可用数据，以1）改善 识别ARDS，和2）识别最有可能从额外的ARDS治疗中受益的高危ARDS患者。 对于这项任务，我们将利用现有的VWD数据集，来自500多名接受机械通气的患者， 包括156例确诊的ARDS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，我们将通过添加 客观EMR数据和从VWD提取的附加特征，例如患者呼吸顺应性和气道 阻力我们的下一个重点将是根据柏林标准预测插管患者的ARDS严重程度恶化。所以 在目标2中，我们将评估预测ARDS严重程度增加的最佳工具，以及哪种类型的时间 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的模型开发， 电子病历，沿着先进的分析技术，将进一步提高ARDS的诊断，并使预测 ARDS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， 可用于提高自动化ARDS诊断的技术水平。我们的预测建模还将实现更大的 深入了解医生可以进行临床干预以阻止ARDS诱导的生理性 恶化最终，这些创新可以通过快速检测ARDS来挽救生命，并提醒医生开始 或基于患者病理生理状态强化ARDS集中治疗。

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.