title: Metabolic Profiling in Patients with Pneumonia on Intensive Care.
abstract: Clinical features and investigations lack predictive value when diagnosing pneumonia, especially when patients are ventilated and when patients develop ventilator associated pneumonia (VAP). New tools to aid diagnosis are important to improve outcomes. This pilot study examines the potential for metabolic profiling to aid the diagnosis in critical care. In this prospective observational study ventilated patients with brain injuries or pneumonia were recruited in the intensive care unit and serum samples were collected soon after the start of ventilation. Metabolic profiles were produced using 1D1H NMR spectra. Metabolic data were compared using multivariate statistical techniques including Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). We recruited 15 patients with pneumonia and 26 with brain injuries, seven of whom went on to develop VAP. Comparison of metabolic profiles using OPLS-DA differentiated those with pneumonia from those with brain injuries (R2Y=0.91, Q2Y=0.28, p=0.02) and those with VAP from those without (R2Y=0.94, Q2Y=0.27, p=0.05). Metabolites that differentiated patients with pneumonia included lipid species, amino acids and glycoproteins. Metabolic profiling shows promise to aid in the diagnosis of pneumonia in ventilated patients and may allow a more timely diagnosis and better use of antibiotics.
authors.affiliation: Section of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK.
authors.forename: David
authors.initials: D
authors.lastname: Antcliffe
authors.affiliation: Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK.
authors.forename: Beatriz
authors.initials: B
authors.lastname: Jiménez
authors.affiliation: Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK.
authors.forename: Kirill
authors.initials: K
authors.lastname: Veselkov
authors.affiliation: Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK.
authors.forename: Elaine
authors.initials: E
authors.lastname: Holmes
authors.affiliation: Section of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK. Electronic address: Anthony.gordon@imperial.ac.uk.
authors.forename: Anthony C
authors.initials: AC
authors.lastname: Gordon
mesh_terms: D000328:Adult; D000368:Aged; D000596:Amino Acids / Q000097:blood; D001930:Brain Injuries / Q000097:blood / Q000150:complications / Q000175:diagnosis; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D016018:Least-Squares Analysis; D008074:Lipoproteins / Q000097:blood; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D055432:Metabolomics; D008875:Middle Aged; D010865:Pilot Projects; D011014:Pneumonia / Q000097:blood / Q000175:diagnosis*; D053717:Pneumonia, Ventilator-Associated / Q000097:blood / Q000150:complications / Q000175:diagnosis*; D025341:Principal Component Analysis; D011446:Prospective Studies
chemical_list: D000596:Amino Acids; D008074:Lipoproteins
keywords: Brain injury; Intensive care; Metabonomics; Pneumonia; Ventilation; Ventilatior associated pneumonia (VAP)