Bengt Gårdlund1, Natalia O Dmitrieva2, Carl F Pieper3, Simon Finfer4, John C Marshall5, B Taylor Thompson6. 1. Department of Infectious Diseases, Karolinska Hospital, S-14186 Stockholm, Sweden. Electronic address: bengt.gardlund@gmail.com. 2. Center for the study of Aging and Human Development, Department of Biostatistics and Bioinformatics, Northern Arizona University, PO Box 15106, Flagstaff, AZ 86011, United States. Electronic address: Natalia.Dmitrieva@nau.edu. 3. Center on Aging, Dept. of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27710, United States. Electronic address: carl.pieper@duke.edu. 4. The George Institute for Global Health, University of New South Wales, Level 5, 1 King Street, Newtown, NSW 2042, Australia. Electronic address: sfinfer@georgeinstitute.org.au. 5. University of Toronto, Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON M5B 1W8, Canada. Electronic address: marshallj@smh.ca. 6. Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02140, United States. Electronic address: TTHOMPSON1@mgh.harvard.edu.
Abstract
PURPOSE: Septic shock is a highly heterogeneous condition which is part of the challenge in its diagnosis and treatment. In this study we aim to identify clinically relevant subphenotypes of septic shock using a novel statistic al approach. METHODS: Baseline patient data from a large global clinical trial of septic shock (n = 1696) was analysed using latent class analysis (LCA). This approach allowed investigators to identify subgroups in a heterogeneous population by estimating a categorical latent variable that detects relatively homogeneous subgroups within a complex phenomenon. RESULTS: LCA identified six different, clinically meaningful subphenotypes of septic shock each with a typical profile: (1) "Uncomplicated Septic Shock, (2) "Pneumonia with adult respiratory distress syndrome (ARDS)", (3) "Postoperative Abdominal", (4) "Severe Septic Shock", (5): "Pneumonia with ARDS and multiple organ dysfunction syndrome (MODS)", (6) "Late Septic Shock". The 6-class solution showed high entropy approaching 1 (i.e., 0.92), indicating there was excellent separation between estimated classes. CONCLUSIONS: LCA appears to be an applicable statistical tool in analysing a heterogenous clinical cohort of septic shock. The results may lead to a better understanding of septic shock complexity and form a basis for considering targeted therapies and selecting patients for future clinical trials.
PURPOSE:Septic shock is a highly heterogeneous condition which is part of the challenge in its diagnosis and treatment. In this study we aim to identify clinically relevant subphenotypes of septic shock using a novel statistic al approach. METHODS: Baseline patient data from a large global clinical trial of septic shock (n = 1696) was analysed using latent class analysis (LCA). This approach allowed investigators to identify subgroups in a heterogeneous population by estimating a categorical latent variable that detects relatively homogeneous subgroups within a complex phenomenon. RESULTS: LCA identified six different, clinically meaningful subphenotypes of septic shock each with a typical profile: (1) "Uncomplicated Septic Shock, (2) "Pneumonia with adult respiratory distress syndrome (ARDS)", (3) "Postoperative Abdominal", (4) "Severe Septic Shock", (5): "Pneumonia with ARDS and multiple organ dysfunction syndrome (MODS)", (6) "Late Septic Shock". The 6-class solution showed high entropy approaching 1 (i.e., 0.92), indicating there was excellent separation between estimated classes. CONCLUSIONS: LCA appears to be an applicable statistical tool in analysing a heterogenous clinical cohort of septic shock. The results may lead to a better understanding of septic shock complexity and form a basis for considering targeted therapies and selecting patients for future clinical trials.
Authors: Benjamin Y Andrew; Carl F Pieper; Anne D Cherry; Jane F Pendergast; Jamie R Privratsky; Joseph P Mathew; Mark Stafford-Smith Journal: Ann Thorac Surg Date: 2021-12-28 Impact factor: 5.102
Authors: Kirsten N Kangelaris; Regina Clemens; Xiaohui Fang; Alejandra Jauregui; Tom Liu; Kathryn Vessel; Thomas Deiss; Pratik Sinha; Aleksandra Leligdowicz; Kathleen D Liu; Hanjing Zhuo; Matthew N Alder; Hector R Wong; Carolyn S Calfee; Clifford Lowell; Michael A Matthay Journal: Am J Physiol Lung Cell Mol Physiol Date: 2020-12-23 Impact factor: 5.464
Authors: Juliana F da Silva; Alfonso C Hernandez-Romieu; Sean D Browning; Beau B Bruce; Pavithra Natarajan; Sapna B Morris; Jeremy A W Gold; Robyn Neblett Fanfair; Jessica Rogers-Brown; John Rossow; Christine M Szablewski; Nadine Oosmanally; Melissa Tobin D'Angelo; Cherie Drenzek; David J Murphy; Julie Hollberg; James M Blum; Robert Jansen; David W Wright; William Sewell; Jack Owens; Benjamin Lefkove; Frank W Brown; Deron C Burton; Timothy M Uyeki; Priti R Patel; Brendan R Jackson; Karen K Wong Journal: Open Forum Infect Dis Date: 2020-12-07 Impact factor: 3.835