IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM.

Journal: Shock (Augusta, Ga.)
Published Date:

Abstract

Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprotein levels and lower mortality (previously subphenotyped normolipoprotein phenotype [NORMO]) were described. We developed a simplified clinical algorithm for bedside subphenotype recognition. Methods: We analyzed data from four prospective studies (internal dataset), focusing on HYPO and NORMO subphenotypes. A 1,000-tree random forest classifier and logistic regression models were built, using clinical features to predict subphenotypes. Performance was evaluated by comparing predictions to actual subphenotypes derived from a machine learning model. The model was applied to an external dataset of 281 patients from three French studies. Results: The internal cohort consisted of 386 patients (median age, 63 years; 46% female). Four clinical features (hepatic SOFA, cardiovascular SOFA, low [low-density lipoprotein cholesterol {LDL-C}] and high-density lipoprotein cholesterol [high-density lipoprotein cholesterol {HDL-C}]) predicted HYPO versus NORMO subphenotypes with an area under the receiver operating characteristic curve of 0.86, a sensitivity of 0.771, and a specificity of 0.779. In the internal dataset, 28-day mortality for HYPO versus NORMO patients was 26% versus 15%, and in the external cohort, 30% versus 10%. HYPO internal versus external dataset LDL-C levels were similar ( P = 0.99), but HDL-C ( P = 0.02) levels were different. Median NORMO internal versus external dataset LDL-C ( P = 0.99) and HDL-C ( P = 0.12) levels were similar. HYPO patients had lower LDL-C, HDL-C and total cholesterol than NORMO patients in both internal and external datasets. Conclusions: Our simplified clinical data algorithm may allow for bedside recognition of septic patients displaying lipid dysregulation subphenotypes. External validation is needed to verify these results.

Authors

  • Guillaume Labilloy
    UF Health Jacksonville, Center for Data Solutions, Jacksonville, Florida.
  • Sébastien Tanaka
  • Lauren Page Black
    Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
  • Beulah Augustin
    Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, Florida.
  • Charlotte Hopson
    Department of Emergency Medicine, University of Florida College of Medicine, Gainesville, Florida.
  • Joanne Bethencourt
    Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, Florida.
  • Dongyuan Wu
  • Dawoud Sulaiman
    Division of Cardiology, Department of Medicine, UCLA David Geffen School of Medicine, Los Angeles, California.
  • Andrew Bertrand
    Department of Emergency Medicine, University of Florida College of Medicine, Gainesville, Florida.
  • Reinaldo Salomão
    Laboratory of Sepsis Research (LPS), Department of Internal Medicine, Escola Paulista de Medicina, Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil.
  • Kiley Graim
    Dept. of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA*Currently at the Flatiron Institute & Princeton University.
  • Susmita Datta
    Department of Biostatistics, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, USA. susmita.datta@ufl.edu.
  • Srinivasa Reddy
    Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, 90095, USA.
  • Faheem W Guirgis
    Department of Emergency Medicine, University of Florida College of Medicine, Gainesville, Florida.
  • Daniel A Hofmaenner
    Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland.