Journal Articles

A community approach to mortality prediction in sepsis via gene expression analysis

February 22, 2018

Timothy E. Sweeney, Thanneer M. Perumal, Ricardo Henao, Marshall Nichols, Judith A. Howrylak, Augustine M. Choi, Jesús F. Bermejo-Martin, Raquel Almansa, Eduardo Tamayo, Emma E. Davenport, Katie L. Burnham, Charles J. Hinds, Julian C. Knight, Christopher W. Woods, Stephen F. Kingsmore, Geoffrey S. Ginsburg, Hector R. Wong, Grant P. Parnell, Benjamin Tang, Lyle L. Moldawer, Frederick E. Moore, Larsson Omberg, Purvesh Khatri, Ephraim L. Tsalik, Lara M. Mangravite & Raymond J. Langley (2018). A community approach to mortality prediction in sepsis via gene expression analysis. Nature Communications 9.

Author Affiliations

Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, 94305, USA
Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
Sage Bionetworks, Seattle, WA, 98109, USA
Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27708, USA
Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA
Division of Pulmonary and Critical Care Medicine, Penn State Milton S. Hershey Medical Center, Hershey, PA, 17033, USA
Department of Medicine, Cornell Medical Center, New York, NY, 10065, USA
Hospital Clínico Universitario de Valladolid/IECSCYL, Valladolid, 47005, Spain
Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University, London, EC1M 6BQ, UK
Division of Infectious Diseases and International Health, Department of Medicine, Duke University, Durham, NC, 27710, USA
Durham Veteran’s Affairs Health Care System, Durham, NC, 27705, USA
Rady Children’s Institute for Genomic Medicine, San Diego, CA, 92123, USA
Division of Critical Care Medicine, Cincinnati Children’s Hospital Medical Center and Cincinnati Children’s Research Foundation, Cincinnati, OH, 45223, USA
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, NSW, 2145, Australia
Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, NSW, 2145, Australia
Department of Intensive Care Medicine, Nepean Hospital, Sydney, Australia, Penrith, NSW, 2751, Australia
Nepean Genomic Research Group, Nepean Clinical School, University of Sydney, Penrith, NSW, 2751, Australia
Marie Bashir Institute for Infectious Diseases and Biosecurity, Westmead, NSW, 2145, Australia
Department of Surgery, University of Florida College of Medicine, Gainesville, FL, 32610, USA
Department of Pharmacology, University of South Alabama, Mobile, AL, 36688, USA

Abstract

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765–0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

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