Acute respiratory distress syndrome (ARDS) is an inflammatory condition of the lung producing severe lung damage. It is one of the most severe forms of acute lung injury and responsible for high mortality (40%) and long-term morbidity [1,2,3]. An estimated 200,000 Americans develop ARDS each year, of which more than 74,000 die from the disease [1]. Patients who survive ARDS experience long-term deficits in physical and neurocognitive function [4, 5]. Both primary hospitalizations and increased health service utilization among survivors are associated with high healthcare costs [1, 4]. For example, the average cost of an ICU (Intensive Care Unit) patient requiring mechanical ventilation ranges between $7000 and $11,000 per day with an incremental cost of $1000–1500 per day for mechanical ventilation [6].

Numerous predisposing factors for ARDS have previously been identified (e.g., sepsis, aspiration, and trauma) [7]. However, our understanding of patient susceptibility to ARDS is incomplete and the disease onset is poorly predicted by current risk models. Among patients with multiple established risk factors, the majority do not develop ARDS, while a minority develop severe, life-threatening disease [8, 9]. The most commonly used ARDS risk model (Lung Injury Prediction Score, LIPS) has strong negative predictive value (97%), effectively identifying patients at low risk for ARDS, but weak positive predictive value (18%) [1, 8], implying poor ability to predict disease onset. The clinical diagnosis of ARDS is established based on the radiological, physiological, and clinical criteria summarized in the Berlin definition (Table 1) [9]. However, those criteria only show a moderate correlation with real-time and post-mortem tissue pathological findings [10, 11] and temporally lag the acute, dynamic inflammatory processes responsible for ARDS, and thus cannot be used for early diagnosis and trajectory monitoring of ARDS. Therefore, there is a significant unmet clinical need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS.

Table 1 The Berlin definition of acute respiratory distress syndrome (ARDS) Full size table

Exhaled breath condensate (non-volatile compounds) of ARDS patients have been studied actively for years to help understanding of the natural history, pathophysiology, and prognosis of ARDS [12, 13]. For example, a novel and non-invasive sampling method using heat-moisture exchanger (HME) filter [14] was developed very recently to accurately sample the distal airspace in patients with ARDS. The HME filter is an inline hygroscopic sponge placed between the patient and the ventilator, the moisture from the patient’s exhaled breath condenses on this filter. By clinical practice, this filter should be changed every few hours, then the condensed fluid can be collected from the used filter and analyzed using liquid chromatography coupled tandem mass spectrometry (LC-MS). While potentially useful in ARDS diagnosis, this technology is focused on proteomic analysis of the breath condensates and requires long analysis time.

Hundreds of volatile organic compounds (VOCs) are also contained in exhaled breath. Many VOCs (such as pentane, isoprene, and ethane) are related to inflammatory processes occurring in the lungs and systemically in blood from remote organ injury [15,16,17,18]. Those and other VOCs could potentially be used as biomarkers to predict the onset and severity of certain critical lung diseases such as ARDS as well as systemic inflammation such as sepsis. They also could help guide therapy if they could be measured simultaneously and precisely in real-time [19,20,21,22]. Unlike blood-based analysis, breath is unlimited in its sampling potential and can be non-invasively and continuously collected and analyzed. Technologies designed for the real-time analysis of VOCs in a point-of-care (POC) fashion could allow for the identification of breathomic signatures that enable the early diagnosis of ARDS, stratification, and trajectory monitoring, allowing for precision treatments.

Table 2 summarizes the major technologies used in breath analysis. A more comprehensive overview of the different technologies can also be found in Saalberg et al. [35] and Cao et al. [36]. Gas chromatography in tandem with mass spectrometer (GC-MS) is the gold standard for the analysis of complex vapor mixtures such as breath samples. In practice, breath from a subject is collected in a thermal desorption tube or sampling bag, and then transferred to GC-MS by thermal desorption device or by solid phase microextraction (SPME). Comprehensive 2-dimensional (2D) GC has improved the peak capacity over the traditional GC [37, 38]. VOC analytes are subject to two independent separation processes, first by their vapor pressures in the 1st-dimensional column and then by their polarities in the 2nd-dimensional column. It has also been used for detection of diseases such as cancer, tuberculosis, and human volatome [39,40,41]. Due to the bulky size and the long turn-around times, GC-MS and comprehensive 2D GC are not suitable for POC applications and cannot be used to continuously monitor the subject to detect dynamic changes. SIFT-MS (selected-ion flow-tube mass spectrometry) and PTR-MS (proton transfer reaction tube mass spectrometry) has high sensitivity and can be used for real-time breath VOC monitoring [25,26,27]. However, the bulky size, heavy weight (> 200 kg), and high cost limit its wide acceptance. Ion mobility spectrometry (IMS) [29,30,31] can be operated under ambient pressure, thus avoid the use of a cumbersome vacuum pump. The portability and short analysis time (usually a few minutes) makes IMS suitable for POC application. Recently exploratory tests using FAIMS (Field Asymmetric Ion Mobility Spectrometry) technology in diagnosis of lung cancer, asthma, and inflammatory bowel disease have been reported [30, 31]. However, its limited VOC separation capability may affect the diagnostic accuracy. Electronic nose (e-nose) relies on varied vapor sensor arrays (such as colorimetric, gold nanoparticles, carbon nanotubes) and pattern recognition for breath analysis [21, 32, 33]. Although portable, fast, and easy to use, e-nose has poor chemical selectivity, device-to-device repeatability, and stability, as well as high susceptibility to background or interference VOCs [21, 22]. Portable GC systems are also used in breath analysis [42]. However, current commercial portable GC systems are 1D devices and have limited separation capability (or peak capacity), which, again, may affect the diagnostic accuracy for given diseases. In addition, most of the 1D GC devices are not customized to operate in a fully automated mode, which hinders its clinical applications.

Table 2 A summary of breath analysis technologies Full size table

Recently, we have developed a fully automated portable GC device that can be operated simultaneously as 1D GC and comprehensive 2D GC with a sub-ppb sensitivity [43]. With the help of the 2-dimensional separation, the co-eluted peaks that are not separated from the 1st-dimensional column can further be separated on the 2nd-dimensional column, thus increasing device’s separation capability. The aim of this study was to further adapt this portable GC for the use on a mechanical ventilator in ICUs and develop the related algorithms for rapid analysis of breath from patients undergoing mechanical ventilation, in order to understand the ability of our GC (and the algorithms) to detect the presence of ARDS compared to clinician adjudication.

Figure 1 shows the schematic of the GC device connected to a ventilator. In our work breath was collected and analyzed every 33 min via a small tube connected to the exhalation port of the ventilator. A total of 97 peaks were separated out from human breath. Through machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA), 9 out of 97 peaks were selected as a VOC subset for the discrimination between ARDS and non-ARDS respiratory failure. Forty-eight (48) ARDS and non-ARDS patients with a total of 85 different breath chromatograms were evaluated. Among all 48 patients, we used 28 patients (43 sets of breath) as the training set and 20 patients (42 sets of breath) as the testing set. Using blinded physician adjudication of the patients’ records based on the Berlin criteria as the gold standard, our breath analysis achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value.