PROJECT SUMMARY / ABSTRACT Acute respiratory distress syndrome (ARDS) is common, costly, and responsible for high mortality and long- term morbidity. Unfortunately, our understanding of patient susceptibility and the underlying pathobiology of ARDS is incomplete, resulting in imprecise ARDS diagnosis and the inability to accurately track its progression. Exhaled breath contains hundreds of volatile organic and inorganic compounds (VOCs and VICs), many of which are related to inflammatory and metabolic processes in the lungs and other organs. The goal of the proposed project is to refine and test a novel, portable micro-gas chromatography (micro-GC) device that can non-invasively and serially sample the lower respiratory tract for VOCs and VICs at the point of care. The overarching hypothesis is that exhaled VOCs/VICs will exhibit dynamic patterns that can indicate the presence of ARDS and track the disease trajectory. The team has developed a preliminary portable micro-GC device and used it to measure exhaled VOCs, and they discovered VOC patterns distinguishing ARDS from other causes of acute hypoxic respiratory failure in a small group of adult patients. To advance the device for clinical use, they must now validate these preliminary findings and obtain robust time series data to identify the breath biomarkers that enable disease trajectory monitoring. They will also evaluate the added benefit of measuring VICs within the system, which they expect will improve device accuracy by providing additional inflammation markers. In Aim 1, they will validate VOC breath biomarkers that identify ARDS patients and predict changes in clinical status and outcomes. First, they will quickly refine their preliminary GC devices to enhance clinical utility in exhaled VOC measurement. Then they will prospectively recruit 400 patients with acute hypoxic respiratory failure and longitudinally measure VOCs and obtain other clinical and physiologic data for up to 10 days. They will also develop algorithms for GC signal analysis and test the algorithms that use VOC patterns to identify ARDS. They will further validate algorithms that use dynamic changes in VOC patterns to predict clinical trajectory. In Aim 2, they will Identify additional VIC breath biomarkers using new VIC detection modules and develop enhanced breath signatures using both VOCs and VICs to detect ARDS and monitor its trajectory. Once they integrate the new VIC detection modules into the existing GC devices, they will collect VICs in patients recruited into the ongoing prospective longitudinal (in addition to VOCs). They will again leverage machine learning approaches to develop and validate algorithms that identify ARDS from VOC/VIC patterns, compare with algorithms using VOCs alone, and track dynamic changes to predict changes in clinical status and outcomes. The innovative point-of-care gas analyzer will bring molecular diagnostics to the bedside, enabling more accurate ARDS diagnosis that allows f...