Octane in exhaled breath to diagnose acute respiratory distress syndrome in invasively ventilated intensive care unit patients

Background The concentration of exhaled octane has been postulated as a reliable biomarker for acute respiratory distress syndrome (ARDS) using metabolomics analysis with gas chromatography and mass spectrometry (GC-MS). A point-of-care (POC) breath test was developed in recent years to accurately measure octane at the bedside. The aim of the present study was to validate the diagnostic accuracy of exhaled octane for ARDS using a POC breath test in invasively ventilated intensive care unit (ICU) patients. Methods This was an observational cohort study of consecutive patients receiving invasive ventilation for at least 24 h, recruited in two university ICUs. GC-MS and POC breath tests were used to quantify the exhaled octane concentration. ARDS was assessed by three experts following the Berlin definition and used as the reference standard. The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy. Results 519 patients were included and 190 (37%) fulfilled the criteria for ARDS. The median (interquartile range) concentration of octane using the POC breath test was not significantly different between patients with ARDS (0.14 (0.05–0.37) ppb) and without ARDS (0.11 (0.06–0.26) ppb; p=0.64). The AUC for ARDS based on the octane concentration in exhaled breath using the POC breath test was 0.52 (95% CI 0.46–0.57). Analysis of exhaled octane with GC-MS showed similar results. Conclusions Octane in exhaled breath has insufficient diagnostic accuracy for ARDS. This disqualifies the use of octane as a biomarker in the diagnosis of ARDS and challenges most of the research performed up to now in the field of exhaled breath metabolomics

Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts

Background: Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches. Methods: PRoVENT–COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov, NCT04346342. Findings: Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent class analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO2/FiO2, pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0·10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0–15 vs 5, 0–17; p=0·016) and more frequent venous thrombotic events (109 [32%] of 336 patients vs 176 [26%] of 671 patients; p=0·048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day mortality (OR 1·64, 95% CI 1·17–2·29 for ventilatory ratio; 1·82, 1·24–2·66 for mechanical power). The association between upward ventilatory ratio trajectories (trajectory B) and 28-day mortality was confirmed in the replication cohorts (OR 4·65, 95% CI 1·87–11·6 for ventilatory ratio in replication cohort 1; 1·89, 1·05–3·37 for ventilatory ratio in replication cohort 2). Interpretation: At baseline, COVID-19-related ARDS has no consistent respiratory subphenotype. Patients diverged from a fairly homogenous to a more heterogeneous population, with trajectories of ventilatory ratio and mechanical power being the most discriminatory. Modelling these parameters alone provided prognostic value for duration of mechanical ventilation and mortality. Funding: Amsterdam UMC