Development of a biomarker mortality risk model in acute respiratory distress syndrome.
Pubmed ID: 31842964
Pubmed Central ID: PMC6916252
Journal: Critical care (London, England)
Publication Date: 12/16/2019
Affiliation: College of Medicine, University of Arizona Health Sciences, Tucson, AZ, USA. firstname.lastname@example.org.
MeSH Terms: Humans, Male, Adult, Female, Logistic Models, Middle Aged, Risk Assessment, Peptide Fragments, Cytokines, APACHE, Interleukin-6, Interleukin-8, Biomarkers, Intramolecular Oxidoreductases, Latent Class Analysis, Respiratory Distress Syndrome, Interleukin 1 Receptor Antagonist Protein, Interleukin-1beta, Macrophage Migration-Inhibitory Factors, Nicotinamide Phosphoribosyltransferase, Sphingosine-1-Phosphate Receptors, Vesicular Transport Proteins
Grants: K08 HL141623, R42 HL145930, R41 HL147769, P01 HL126609, P01 HL134610
Authors: Bime C, Garcia JGN, Casanova N, Lussier Y, Oita RC, Ndukum J, Lynn H, Camp SM, Abraham I, Carter D, Miller EJ, Mekontso-Dessap A, Downs CA
Cite As: Bime C, Casanova N, Oita RC, Ndukum J, Lynn H, Camp SM, Lussier Y, Abraham I, Carter D, Miller EJ, Mekontso-Dessap A, Downs CA, Garcia JGN. Development of a biomarker mortality risk model in acute respiratory distress syndrome. Crit Care 2019 Dec 16;23(1):410.
BACKGROUND: There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome. METHODS: This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality. RESULTS: From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04). CONCLUSIONS: An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization.