A Clinical Surveillance Software Platform for Early Identification of Severe Asynchrony in Mechanically Ventilated Patients in the Intensive Care Unit

重症监护室（ICU）中12-30%的通气患者会发生严重的患者-呼吸机阻塞， 在美国，每年约有200，000 - 500，000名患者。许多患者将变得与 当机器（呼吸机）试图将空气移入呼吸机时， 肺，反之亦然。严重严重，其中严重指数（量化异步 呼吸）超过10%，与ICU死亡率增加5倍相关，并与6天额外时间相关 机械通气。换句话说，在美国，患有严重抑郁症的患者需要额外支付5 -12美元 10亿美元的重症监护费用。目前，不存在商业上可获得的软件来检测真实的中的放射性。 时间解决问题的第一步是承认，不幸的是，由于 由于对医疗保健提供者的限制，患者可能在被医疗保健提供者识别之前就已经“与呼吸机作斗争”。 提供商此外，研究表明，临床医生在检测梅毒时的敏感性很差， 波形分析我们的总体目标是帮助呼吸治疗师识别严重的 提高波形解释的准确性和速度， 需要解决的问题。我们的具体目标是：1.开发和测试临床监测 呼吸治疗师监测多个患者非同步性的仪表板。在这一具体目标中， 我们将扩展Syncron-ETM软件，以分析来自多个转发器的数据。此外，我们将执行一项 使用真实的患者数据进行模拟研究，其中两名呼吸治疗师将协助进行概念验证临床试验 效用测试在情景A中，呼吸治疗师将监测10名患者， 提供资料。在场景B中，另一组10名患者（具有相似的行为）被 在没有任何信息的情况下监控。呼吸治疗师被要求识别 严重过敏（过敏指数>10%）超过5分钟。最后，我们将比较 正确检测到的严重癫痫发作次数（比较灵敏度和特异性）， 这种检测的时间。2.培养协助呼吸治疗师提高 波形解释在这一具体目标中，我们建议开发一种辅助呼吸的能力， 治疗师在解释波形更准确和更迅速。为了确保临床采用， 我们打算避免采用“黑箱”处理方法。具体而言，我们打算向用户提供充分的信息 并允许用户通过“审计”系统来做出关于安全性的最终决定。一是 添加可视注释波形和突出显示检测到的“地标”的功能。接下来，我们将表演一个 基于先前收集的患者数据的模拟研究，其中两名呼吸治疗师将审查 两种情况下的波形，以检测基于他们的临床判断。我们将比较 这两种情况之间的免疫检测的灵敏度和特异性以及完成的时间。

Severe patient-ventilator asynchrony affects 12-30% of ventilated patients in the intensive care unit (ICU) or approximately 200,000-500,000 patients annually in the US. Many patients will become asynchronous with the ventilator and will be attempting to exhale when the machine (ventilator) is attempting to move air into the lungs, and vice versa. Severe asynchrony, where asynchrony index (quantifying the fraction of asynchronous breaths) exceeds 10%, is associated with a 5x increase in ICU mortality and was associated with 6 extra days of mechanical ventilation. In other words, in the US, patients with severe asynchrony incur an extra $5-12 billion of critical care costs. Currently, no commercially available software exists to detect asynchrony in real- time. The first step needed to address the problem of asynchrony is recognition, and unfortunately, due to constraints on healthcare providers, patients may be “fighting the ventilator” well before recognition by the providers. In addition, studies show that clinicians have a poor sensitivity in detecting asynchrony using waveform analysis. Our overall goal is to assist respiratory therapists in identifying episodes of severe asynchrony earlier and improving their accuracy and speed in interpreting waveforms, which are major steps required to address asynchrony. Our specific aims are: 1. Developing and Testing a Clinical Surveillance Dashboard for Respiratory Therapists to Monitor Asynchrony in Multiple Patients. In this specific aim, we will extend the Syncron-ETM software to analyze data from multiple ventilators. In addition, we will perform a simulation study with real patient data, where two respiratory therapists will assist in a proof-of-concept clinical utility testing. In Scenario A, the respiratory therapists will monitor 10 patients where the asynchrony information is provided. In Scenario B, another set of 10 patients (with similar asynchrony behavior) are monitored without any information on asynchrony. Respiratory therapists are asked to identify episodes of severe asynchrony (asynchrony index>10%) for a period of more than 5 minutes. In the end, we will compare the number of correctly detected episodes of severe asynchrony (comparing sensitivity and specificity) and timing of such detections. 2. Developing the Capability to Assist Respiratory Therapists in Improving Waveform Interpretation. In this specific aim, we propose to develop a capability to assist respiratory therapists in the interpretation of waveforms more accurately and rapidly. In order to ensure clinical adoption, we intend to avoid a “black box” approach. Specifically, we intend to provide adequate information to the user and allow the user to make the ultimate decision regarding asynchrony by “auditing” the system. First, we will add a capability to visually annotate waveforms and highlight detected “landmarks”. Next, we will perform a simulation study based on previously collected patient data, where two respiratory therapists will review waveforms in two scenarios to detect asynchrony based on their clinical judgement. We will compare the sensitivity and specificity of asynchrony detection and time to completion between the two scenarios.