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

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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.