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

用于早期识别重症监护病房机械通气患者严重不同步的临床监测软件平台

• 批准号: 10079676
• 负责人: Behnood Gholami
• 金额: $ 29.99万
• 依托单位: AUTONOMOUS HEALTHCARE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10079676
• 关键词: Address; Adoption; Affect; Air; Algorithms; Behavior monitoring; Caring; Clinical; Computer software; Critical Care; Data; Detection; Early identification; Ensure; Ethics; Exhalation; Goals; Health Personnel; Intensive Care Units; Judgment; Lung; Mechanical ventilation; Mechanics; Monitor; Patient Monitoring; Patients; Phase; Plant Roots; Prevalence; Provider; Research Personnel; Sensitivity and Specificity; Speed; Stress; System; Tablets; Technology; Testing; Time; Ventilator; Work; base; care costs; dashboard; fighting; improved; indexing; mortality; prototype; respiratory; respiratory assist; simulation; tool

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.

严重的患者访问者异步会影响重症监护病房（ICU）或 在美国，每年约有200,000-500,000名患者。许多患者会因 呼吸机，将试图在机器（呼吸机）试图将空气移入的时候呼气 肺，反之亦然。严重异步，其中异步指数（量化异步的分数 呼吸）超过10％，与ICU死亡率增加5倍有关，并与6天相关 机械通气。换句话说，在美国，患有严重异步的患者会增加5-12美元 十亿个重症监护费用。目前，尚无商业可用的软件来检测现实中的异步 时间。解决异步问题所需的第一步是识别，不幸的是，由于 对医疗保健提供者的限制，患者可能会在认可之前就“与呼吸机作斗争” 提供者。此外，研究表明，临床医生在检测异步的敏感性较差 波形分析。我们的总体目标是协助呼吸治疗师确定严重的发作 异步提前并提高其在解释波形方面的准确性和速度，这是主要步骤 需要解决异步。我们的具体目的是：1。开发和测试临床监测 用于呼吸治疗师的仪表板，以监测多名患者的异步。在这个特定目标中 我们将扩展Syncron-ETM软件以分析来自多个呼吸机的数据。此外，我们将执行 使用真实患者数据的模拟研究，其中两名呼吸治疗师将有助于概念证明临床证明 实用程序测试。在方案A中，呼吸治疗师将监测10例异步的患者 提供了信息。在情况B中，另一组10例患者（异步行为相似）是 没有任何有关异步信息的信息。要求呼吸治疗师确定 严重的异步（异步指数> 10％）超过5分钟。最后，我们将比较 严重异步（比较灵敏度和特异性）的正确检测到的发作的数量 这种检测的时间。 2。开发协助呼吸治疗师改善的能力 波形解释。在这个具体目的中，我们建议开发一种能力来协助呼吸道 在波形的解释中，治疗师更准确，快速地解释。为了确保临床采用， 我们打算避免使用“黑匣子”方法。具体来说，我们打算向用户提供足够的信息 并允许用户通过“审核”系统做出有关异步的最终决定。首先，我们会的 添加视觉注释波形并突出显示的“地标”的功能。接下来，我们将执行 基于先前收集的患者数据的仿真研究，其中两名呼吸治疗师将审查 波形在两种情况下根据其临床判断检测异步。我们将比较 两种情况之间的异步检测和完成时间的灵敏度和特异性。