1
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Whitfield NN, Hogan CA, Chenoweth J, Hansen J, Hsu EB, Humphries R, Mann E, May L, Michelson EA, Rothman R, Self WH, Smithline HA, Karita HCS, Steingrub JS, Swedien D, Weissman A, Wright DW, Liesenfeld O, Shapiro NI. A standardized protocol using clinical adjudication to define true infection status in patients presenting to the emergency department with suspected infections and/or sepsis. Diagn Microbiol Infect Dis 2024; 110:116382. [PMID: 38850687 DOI: 10.1016/j.diagmicrobio.2024.116382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 06/10/2024]
Abstract
In absence of a "gold standard", a standardized clinical adjudication process was developed for a registrational trial of a transcriptomic host response (HR) test. Two physicians independently reviewed clinical data to adjudicate presence and source of bacterial and viral infections in emergency department patients. Discordant cases were resolved by a third physician. Agreement among 955 cases was 74.1% (708/955) for bacterial, 75.6% (722/955) for viral infections, and 71.2% (680/955) overall. Most discordances were minor (85.2%; 409/480) versus moderate (11.7%; 56/480) or complete (3.3%; 16/480). Concordance levels were lowest for bacterial skin and soft tissue infections (8.2%) and for viral respiratory tract infections (4.5%). This robust adjudication process can be used to evaluate HR tests and other diagnostics by regulatory agencies and for educating clinicians, laboratorians, and clinical researchers. Clinicaltrials.gov NCT04094818. SUMMARY: Without a gold standard for evaluating host response tests, clinical adjudication is a robust reference standard that is essential to determine the true infection status in diagnostic registrational clinical studies.
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Affiliation(s)
| | | | - James Chenoweth
- Department of Emergency Medicine, University of California-Davis School of Medicine, Sacramento, California, USA
| | - Jonathan Hansen
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Edbert B Hsu
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Roger Humphries
- Department of Emergency Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Edana Mann
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Larissa May
- Department of Emergency Medicine, University of California-Davis School of Medicine, Sacramento, California, USA
| | - Edward A Michelson
- Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, Department of Emergency Medicine, El Paso, Texas, USA
| | - Richard Rothman
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Wesley H Self
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Howard A Smithline
- Department of Emergency Medicine, University of Massachusetts Chan Medical School - Baystate, Springfield, Massachusetts, USA
| | | | - Jay S Steingrub
- Department of Critical Care Medicine, University of Massachusetts Chan Medical School - Baystate, Springfield, Massachusetts, USA
| | - Daniel Swedien
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Alexandra Weissman
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - David W Wright
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, Georgia
| | | | - Nathan I Shapiro
- Beth Israel Deaconess Medical Center, Emergency Medicine, Boston, Massachusetts, USA
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2
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Halder A, Liesenfeld O, Whitfield N, Uhle F, Schenz J, Mehrabi A, Schmitt FCF, Weigand MA, Decker SO. A 29-mRNA host-response classifier identifies bacterial infections following liver transplantation - a pilot study. Langenbecks Arch Surg 2024; 409:185. [PMID: 38865015 PMCID: PMC11169022 DOI: 10.1007/s00423-024-03373-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 06/01/2024] [Indexed: 06/13/2024]
Abstract
PURPOSE Infections are common complications in patients following liver transplantation (LTX). The early diagnosis and prognosis of these infections is an unmet medical need even when using routine biomarkers such as C-reactive protein (CRP) and procalcitonin (PCT). Therefore, new approaches are necessary. METHODS In a prospective, observational pilot study, we monitored 30 consecutive patients daily between days 0 and 13 following LTX using the 29-mRNA host classifier IMX-BVN-3b that determine the likelihood of bacterial infections and viral infections. True infection status was determined using clinical adjudication. Results were compared to the accuracy of CRP and PCT for patients with and without bacterial infection due to clinical adjudication. RESULTS Clinical adjudication confirmed bacterial infections in 10 and fungal infections in 2 patients. 20 patients stayed non-infected until day 13 post-LTX. IMX-BVN-3b bacterial scores were increased directly following LTX and decreased until day four in all patients. Bacterial IMX-BVN-3b scores detected bacterial infections in 9 out of 10 patients. PCT concentrations did not differ between patients with or without bacterial, whereas CRP was elevated in all patients with significantly higher levels in patients with bacterial infections. CONCLUSION The 29-mRNA host classifier IMX-BVN-3b identified bacterial infections in post-LTX patients and did so earlier than routine biomarkers. While our pilot study holds promise future studies will determine whether these classifiers may help to identify post-LTX infections earlier and improve patient management. CLINICAL TRIAL NOTATION German Clinical Trials Register: DRKS00023236, Registered 07 October 2020, https://drks.de/search/en/trial/DRKS00023236.
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Affiliation(s)
- Amelie Halder
- Heidelberg University, Medical Faculty Heidelberg, Department of Anesthesiology, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | | | | | - Florian Uhle
- Heidelberg University, Medical Faculty Heidelberg, Department of Anesthesiology, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Judith Schenz
- Heidelberg University, Medical Faculty Heidelberg, Department of Anesthesiology, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Arianeb Mehrabi
- Heidelberg University, Medical Faculty Heidelberg, Department of General, Visceral & Transplantation Surgery, Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Felix C F Schmitt
- Heidelberg University, Medical Faculty Heidelberg, Department of Anesthesiology, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Markus A Weigand
- Heidelberg University, Medical Faculty Heidelberg, Department of Anesthesiology, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Sebastian O Decker
- Heidelberg University, Medical Faculty Heidelberg, Department of Anesthesiology, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
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3
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Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024; 56:1293-1321. [PMID: 38871816 PMCID: PMC11263376 DOI: 10.1038/s12276-024-01243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
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Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
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4
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Ivaska L, Herberg J, Sadarangani M. Distinguishing community-acquired bacterial and viral meningitis: Microbes and biomarkers. J Infect 2024; 88:106111. [PMID: 38307149 DOI: 10.1016/j.jinf.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
Diagnostic tools to differentiate between community-acquired bacterial and viral meningitis are essential to target the potentially lifesaving antibiotic treatment to those at greatest risk and concurrently spare patients with viral meningitis from the disadvantages of antibiotics. In addition, excluding bacterial meningitis and thus decreasing antibiotic consumption would be important to help reduce antimicrobial resistance and healthcare expenses. The available diagnostic laboratory tests for differentiating bacterial and viral meningitis can be divided microbiological pathogen-focussed methods and biomarkers of the host response. Bacterial culture-independent microbiological methods, such as highly multiplexed nucleic acid amplification tests, are rapidly making their way into the clinical practice. At the same time, more conventional host protein biomarkers, such as procalcitonin and C-reactive protein, are supplemented by newer proteomic and transcriptomic signatures. This review aims to summarise the current state and the recent advances in diagnostic methods to differentiate bacterial from viral meningitis.
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Affiliation(s)
- Lauri Ivaska
- Department of Paediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Savitehtaankatu 5, 20521 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Kiinanmyllynkatu 10, 20520 Turku, Finland.
| | - Jethro Herberg
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, Norfolk Place, London, United Kingdom.
| | - Manish Sadarangani
- Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada; Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada.
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5
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Tong-Minh K, Daenen K, Endeman H, Ramakers C, Gommers D, van Gorp E, van der Does Y. Performance of the FebriDx Rapid Point-of-Care Test for Differentiating Bacterial and Viral Respiratory Tract Infections in Patients with a Suspected Respiratory Tract Infection in the Emergency Department. J Clin Med 2023; 13:163. [PMID: 38202172 PMCID: PMC10779507 DOI: 10.3390/jcm13010163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/13/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
FebriDx is a rapid point-of-care test combining qualitative measurements of C-reactive protein (CRP) and Myxovirus Resistance Protein A (MxA) using a disposable test device to detect and differentiate acute bacterial from viral respiratory tract infections. The goal of this study was to investigate the diagnostic accuracy of FebriDx in patients with suspected respiratory tract infections in the emergency department (ED). This was an observational cohort study, performed in the ED of an academic hospital. Patients were included if they had a suspected infection. The primary outcome was the presence of a bacterial or viral infection, determined by clinical adjudication by an expert panel. The sensitivity, specificity, and positive and negative predictive value of FebriDx for the presence of bacterial versus non-bacterial infections, and viral versus non-viral infections were calculated. Between March 2019 and November 2020, 244 patients were included. A bacterial infection was present in 41%, viral infection was present in 24%, and 4% of the patients had both viral and bacterial pathogens. FebriDx demonstrated high sensitivity in the detection of bacterial infection (87%), high NPV (91%) to rule out bacterial infection, and high specificity (94%) for viral infection in patients with a suspected infection in the ED.
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Affiliation(s)
- Kirby Tong-Minh
- Department of Emergency Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (K.T.-M.); (Y.v.d.D.)
- Department of Viroscience, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands;
| | - Katrijn Daenen
- Department of Viroscience, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands;
- Department of Intensive Care, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (H.E.); (D.G.)
| | - Henrik Endeman
- Department of Intensive Care, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (H.E.); (D.G.)
| | - Christian Ramakers
- Department of Clinical Chemistry, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands;
| | - Diederik Gommers
- Department of Intensive Care, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (H.E.); (D.G.)
| | - Eric van Gorp
- Department of Viroscience, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands;
- Department of Internal Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Yuri van der Does
- Department of Emergency Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (K.T.-M.); (Y.v.d.D.)
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6
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Yang Q, Meyerson NR, Paige CL, Morrison JH, Clark SK, Fattor WT, Decker CJ, Steiner HR, Lian E, Larremore DB, Perera R, Poeschla EM, Parker R, Dowell RD, Sawyer SL. Human mRNA in saliva can correctly identify individuals harboring acute infection. mBio 2023; 14:e0171223. [PMID: 37943059 PMCID: PMC10746177 DOI: 10.1128/mbio.01712-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023] Open
Abstract
IMPORTANCE There are a variety of clinical and laboratory criteria available to clinicians in controlled healthcare settings to help them identify whether an infectious disease is present. However, in situations such as a new epidemic caused by an unknown infectious agent, in health screening contexts performed within communities and outside of healthcare facilities or in battlefield or potential biowarfare situations, this gets more difficult. Pathogen-agnostic methods for rapid screening and triage of large numbers of people for infection status are needed, in particular methods that might work on an easily accessible biospecimen like saliva. Here, we identify a small, core set of approximately 70 human genes whose transcripts serve as saliva-based biomarkers of infection in the human body, in a way that is agnostic to the specific pathogen causing infection.
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Affiliation(s)
- Qing Yang
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Nicholas R Meyerson
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Darwin Biosciences, Inc., Boulder, Colorado, USA
| | - Camille L Paige
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Darwin Biosciences, Inc., Boulder, Colorado, USA
| | - James H Morrison
- Division of Infectious Diseases, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Stephen K Clark
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Darwin Biosciences, Inc., Boulder, Colorado, USA
| | - Will T Fattor
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Carolyn J Decker
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
| | - Halley R Steiner
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, USA
| | - Elena Lian
- Center for Vector-Borne Infectious Diseases and Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Daniel B Larremore
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Computer Science, University of Colorado Boulder, Boulder, Colorado, USA
- Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Rushika Perera
- Center for Vector-Borne Infectious Diseases and Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Eric M Poeschla
- Division of Infectious Diseases, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Roy Parker
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
| | - Robin D Dowell
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Computer Science, University of Colorado Boulder, Boulder, Colorado, USA
| | - Sara L Sawyer
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
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7
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Pandya R, He YD, Sweeney TE, Hasin-Brumshtein Y, Khatri P. A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples. Genome Med 2023; 15:64. [PMID: 37641125 PMCID: PMC10463681 DOI: 10.1186/s13073-023-01216-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/27/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood. METHODS We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation. RESULTS Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples. CONCLUSION This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.
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Affiliation(s)
| | - Yudong D. He
- Inflammatix Inc., CA 94085 Sunnyvale, USA
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305 USA
- Allen Institute of Immunology, Seattle, WA USA
| | | | | | - Purvesh Khatri
- Inflammatix Inc., CA 94085 Sunnyvale, USA
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305 USA
- Department of Medicine, Center for Biomedical Informatics Research, School of Medicine, Stanford University, Stanford, CA 94305 USA
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8
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Jeffrey M, Denny KJ, Lipman J, Conway Morris A. Differentiating infection, colonisation, and sterile inflammation in critical illness: the emerging role of host-response profiling. Intensive Care Med 2023; 49:760-771. [PMID: 37344680 DOI: 10.1007/s00134-023-07108-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/22/2023] [Indexed: 06/23/2023]
Abstract
Infection results when a pathogen produces host tissue damage and elicits an immune response. Critically ill patients experience immune activation secondary to both sterile and infectious insults, with overlapping clinical phenotypes and underlying immunological mechanisms. Patients also undergo a shift in microbiota with the emergence of pathogen-dominant microbiomes. Whilst the combination of inflammation and microbial shift has long challenged intensivists in the identification of true infection, the advent of highly sensitive molecular diagnostics has further confounded the diagnostic dilemma as the number of microbial detections increases. Given the key role of the host immune response in the development and definition of infection, profiling the host response offers the potential to help unravel the conundrum of distinguishing colonisation and sterile inflammation from true infection. This narrative review provides an overview of current approaches to distinguishing colonisation from infection using routinely available techniques and proposes matrices to support decision-making in this setting. In searching for new tools to better discriminate these states, the review turns to the understanding of the underlying pathobiology of the host response to infection. It then reviews the techniques available to assess this response in a clinically applicable context. It will cover techniques including profiling of transcriptome, protein expression, and immune functional assays, detailing the current state of knowledge in diagnostics along with the challenges and opportunities. The ultimate infection diagnostic tool will likely combine an assessment of both host immune response and sensitive pathogen detection to improve patient management and facilitate antimicrobial stewardship.
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Affiliation(s)
- Mark Jeffrey
- John V Farman Intensive Care Unit, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Division of Anaesthesia, Department of Medicine, Level 4, Addenbrooke's Hospital, University of Cambridge, Hills Road, Cambridge, CB2 0QQ, UK
| | - Kerina J Denny
- Department of Intensive Care, Gold Coast University Hospital, Southport, QLD, Australia
- School of Medicine, University of Queensland, Herston, Brisbane, Australia
| | - Jeffrey Lipman
- University of Queensland Centre for Clinical Research, The University of Queensland, Brisbane, Australia
- Jamieson Trauma Institute and Intensive Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia
- Nimes University Hospital, University of Montpellier, Nimes, France
| | - Andrew Conway Morris
- John V Farman Intensive Care Unit, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
- Division of Anaesthesia, Department of Medicine, Level 4, Addenbrooke's Hospital, University of Cambridge, Hills Road, Cambridge, CB2 0QQ, UK.
- Division of Immunology, Department of Pathology, University of Cambridge, Cambridge, UK.
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9
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Turgman O, Schinkel M, Wiersinga WJ. Host Response Biomarkers for Sepsis in the Emergency Room. Crit Care 2023; 27:97. [PMID: 36941681 PMCID: PMC10027585 DOI: 10.1186/s13054-023-04367-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023] Open
Abstract
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .
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Affiliation(s)
- Oren Turgman
- Center for Experimental and Molecular Medicine, Amsterdam Institute for Infection and Immunity, Amsterdam UMC, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Michiel Schinkel
- Center for Experimental and Molecular Medicine, Amsterdam Institute for Infection and Immunity, Amsterdam UMC, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Division of Infectious Diseases, Department of Medicine, Amsterdam UMC, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Willem Joost Wiersinga
- Center for Experimental and Molecular Medicine, Amsterdam Institute for Infection and Immunity, Amsterdam UMC, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
- Division of Infectious Diseases, Department of Medicine, Amsterdam UMC, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
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10
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Tsakiroglou M, Evans A, Pirmohamed M. Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis. Front Genet 2023; 14:1100352. [PMID: 36968610 PMCID: PMC10036914 DOI: 10.3389/fgene.2023.1100352] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.
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Affiliation(s)
- Maria Tsakiroglou
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- *Correspondence: Maria Tsakiroglou,
| | - Anthony Evans
- Computational Biology Facility, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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11
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Shojaei M, Chen UI, Midic U, Thair S, Teoh S, McLean A, Sweeney TE, Thompson M, Liesenfeld O, Khatri P, Tang B. Multisite validation of a host response signature for predicting likelihood of bacterial and viral infections in patients with suspected influenza. Eur J Clin Invest 2023; 53:e13957. [PMID: 36692131 DOI: 10.1111/eci.13957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/08/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023]
Abstract
BACKGROUND Indiscriminate use of antimicrobials and antimicrobial resistance is a public health threat. IMX-BVN-1, a 29-host mRNA classifier, provides two separate scores that predict likelihoods of bacterial and viral infections in patients with suspected acute infections. We validated the performance of IMX-BVN-1 in adults attending acute health care settings with suspected influenza. METHOD We amplified 29-host response genes in RNA extracted from blood by NanoString nCounter. IMX-BVN-1 calculated two scores to predict probabilities of bacterial and viral infections. Results were compared against the infection status (no infection; highly probable/possible infection; confirmed infection) determined by clinical adjudication. RESULTS Amongst 602 adult patients (74.9% ED, 16.9% ICU, 8.1% outpatients), 7.6% showed in-hospital mortality and 15.5% immunosuppression. Median IMX-BVN-1 bacterial and viral scores were higher in patients with confirmed bacterial (0.27) and viral (0.62) infections than in those without bacterial (0.08) or viral (0.21) infection, respectively. The AUROC distinguishing bacterial from nonbacterial illness was 0.81 and 0.87 when distinguishing viral from nonviral illness. The bacterial top quartile's positive likelihood ratio (LR) was 4.38 with a rule-in specificity of 88%; the bacterial bottom quartile's negative LR was 0.13 with a rule-out sensitivity of 96%. Similarly, the viral top quartile showed an infinite LR with rule-in specificity of 100%; the viral bottom quartile had a LR of 0.22 and a rule-out sensitivity of 85%. CONCLUSION IMX-BVN-1 showed high accuracy for differentiating bacterial and viral infections from noninfectious illness in patients with suspected influenza. Clinical utility of IMX-BVN will be validated following integration into a point of care system.
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Affiliation(s)
- Maryam Shojaei
- Department of Medicine, Sydney Medical School Nepean, Nepean Hospital, University of Sydney, Penrith, New South Wales, Australia.,Department of Intensive Care Medicine, Nepean Hospital, Penrith, New South Wales, Australia.,Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Uan-I Chen
- Inflammatix, Inc., Sunnyvale, California, USA
| | - Uros Midic
- Inflammatix, Inc., Sunnyvale, California, USA
| | | | - Sally Teoh
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, New South Wales, Australia
| | - Anthony McLean
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, New South Wales, Australia
| | | | | | | | | | - Benjamin Tang
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, New South Wales, Australia.,Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
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Bauer W, Galtung N, von Wunsch-Rolshoven Teruel I, Dickescheid J, Reinhart K, Somasundaram R. Screening auf Sepsis in der Notfallmedizin – qSOFA ist uns nicht genug. Notf Rett Med 2023. [DOI: 10.1007/s10049-022-01078-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Zusammenfassung
Hintergrund
Die Sepsis ist eine häufige und lebensbedrohliche Komplikation einer akuten Infektion. In der Notfallmedizin hat sich zum Screening auf Sepsis der Quick Sequential-Organ-Failure-Assessment(qSOFA)-Score etabliert. Bereits mit der Einführung des Scores wurde dessen schwache Sensitivität kritisiert. Nun fordern aktuelle Leitlinien, den qSOFA-Score nicht mehr zum Screening auf Sepsis einzusetzen. Als eine Alternative wird der National Early Warning Score 2 (NEWS2) vorgeschlagen.
Ziel der Arbeit
In einer Subanalyse einer Kohorte von notfallmedizinischen Patient*innen soll die diagnostische Aussagekraft des qSOFA-Scores und des NEWS2 zur Erkennung einer Sepsis verglichen werden. Zusätzlich soll gezeigt werden, inwieweit mithilfe von abweichenden Vitalparametern bereits eine Risikoerhöhung für eine Sepsis ableitbar ist.
Methodik
Mittels AUROC (Area Under Receiver Operating Characteristics) und Odds Ratios wurden die Scores bzw. die Vitalparameter auf ihre Fähigkeit untersucht, septische Patient*innen zu erkennen.
Ergebnisse
Von 312 eingeschlossenen Patient*innen wurde bei 17,9 % eine Sepsis diagnostiziert. Der qSOFA-Score erkannte eine Sepsis mit einer AUROC von 0,77 (NEWS2 0,81). Für qSOFA fand sich eine Sensitivität von 57 % (Spezifität 83 %), für NEWS2 96 % (Spezifität 45 %). Die Analyse der einzelnen Vitalparameter zeigte, dass unter Patient*innen mit einer akuten Infektion eine Vigilanzminderung als deutliches Warnsignal für eine Sepsis zu werten ist.
Diskussion
In der Notfallmedizin sollte qSOFA nicht als alleiniges Tool für das Screening auf Sepsis verwendet werden. Bei Verdacht auf eine akute Infektion sollten grundsätzlich sämtliche Vitalparameter erfasst werden, um das Vorliegen einer akuten Organschädigung und somit einen septischen Krankheitsverlauf frühzeitig zu erkennen.
Graphic abstract
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Ram-Mohan N, Rogers AJ, Blish CA, Nadeau KC, Zudock EJ, Kim D, Quinn JV, Sun L, Liesenfeld O, Yang S. Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department. Microbiol Spectr 2022; 10:e0230522. [PMID: 36250865 PMCID: PMC9769905 DOI: 10.1128/spectrum.02305-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 09/26/2022] [Indexed: 01/06/2023] Open
Abstract
Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial coinfection, and determining illness severity since current practices require separate workflows. Here, we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and bacterial coinfections and predicting clinical severity of COVID-19. A total of 161 patients with PCR-confirmed COVID-19 (52.2% female; median age, 50.0 years; 51% hospitalized; 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene blood RNA), and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrollment, and the remaining patients oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial coinfection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e., Clostridioides difficile colitis (n = 1), urinary tract infection (n = 1), and clinically diagnosed bacterial infections (n = 3), for a specificity of 99.4%. Two of 101 (2.8%) patients in the IMX-SEV-3 "Low" severity classification and 7/60 (11.7%) in the "Moderate" severity classification died within 30 days of enrollment. IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19 and bacterial coinfections and predicted patients' risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management, including more accurate treatment decisions and optimized resource utilization. IMPORTANCE We assay the utility of the single-test IMX-BVN-3/IMX-SEV-3 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources.
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Affiliation(s)
- Nikhil Ram-Mohan
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Angela J. Rogers
- Department of Medicine—Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Catherine A. Blish
- Department of Medicine/Infectious Diseases, Stanford University School of Medicine, Stanford, California, USA
| | - Kari C. Nadeau
- Department of Medicine—Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Elizabeth J. Zudock
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - David Kim
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - James V. Quinn
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Lixian Sun
- Inflammatix, Inc., Burlingame, California, USA
| | | | - The Stanford COVID-19 Biobank Study Group
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Medicine—Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Medicine/Infectious Diseases, Stanford University School of Medicine, Stanford, California, USA
- Inflammatix, Inc., Burlingame, California, USA
| | - Samuel Yang
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
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Assessment of Infection Progression per Host Gene Expression. Crit Care Med 2022; 50:1834-1837. [PMID: 36394402 DOI: 10.1097/ccm.0000000000005677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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15
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Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department. Eur J Emerg Med 2022; 29:357-365. [PMID: 35467566 PMCID: PMC9432813 DOI: 10.1097/mej.0000000000000931] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND IMPORTANCE mRNA-based host response signatures have been reported to improve sepsis diagnostics. Meanwhile, prognostic markers for the rapid and accurate prediction of severity in patients with suspected acute infections and sepsis remain an unmet need. IMX-SEV-2 is a 29-host-mRNA classifier designed to predict disease severity in patients with acute infection or sepsis. OBJECTIVE Validation of the host-mRNA infection severity classifier IMX-SEV-2. DESIGN, SETTINGS AND PARTICIPANTS Prospective, observational, convenience cohort of emergency department (ED) patients with suspected acute infections. OUTCOME MEASURES AND ANALYSIS Whole blood RNA tubes were analyzed using independently trained and validated composite target genes (IMX-SEV-2). IMX-SEV-2-generated risk scores for severity were compared to the patient outcomes in-hospital mortality and 72-h multiorgan failure. MAIN RESULTS Of the 312 eligible patients, 22 (7.1%) died in hospital and 58 (18.6%) experienced multiorgan failure within 72 h of presentation. For predicting in-hospital mortality, IMX-SEV-2 had a significantly higher area under the receiver operating characteristic (AUROC) of 0.84 [95% confidence intervals (CI), 0.76-0.93] compared to 0.76 (0.64-0.87) for lactate, 0.68 (0.57-0.79) for quick Sequential Organ Failure Assessment (qSOFA) and 0.75 (0.65-0.85) for National Early Warning Score 2 (NEWS2), ( P = 0.015, 0.001 and 0.013, respectively). For identifying and predicting 72-h multiorgan failure, the AUROC of IMX-SEV-2 was 0.76 (0.68-0.83), not significantly different from lactate (0.73, 0.65-0.81), qSOFA (0.77, 0.70-0.83) or NEWS2 (0.81, 0.75-0.86). CONCLUSION The IMX-SEV-2 classifier showed a superior prediction of in-hospital mortality compared to biomarkers and clinical scores among ED patients with suspected infections. No improvement for predicting multiorgan failure was found compared to established scores or biomarkers. Identifying patients with a high risk of mortality or multiorgan failure may improve patient outcomes, resource utilization and guide therapy decision-making.
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Bauer W, Gläser S, Thiemig D, Wanner K, Peric A, Behrens S, Bialas J, Behrens A, Galtung N, Liesenfeld O, Sun L, May L, Mace S, Ott S, Vesenbeckh S. Detection of Viral Infection and Bacterial Coinfection and Superinfection in Coronavirus Disease 2019 Patients Presenting to the Emergency Department Using the 29-mRNA Host Response Classifier IMX-BVN-3: A Multicenter Study. Open Forum Infect Dis 2022; 9:ofac437. [PMID: 36111173 PMCID: PMC9452140 DOI: 10.1093/ofid/ofac437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 08/24/2022] [Indexed: 11/24/2022] Open
Abstract
Background Identification of bacterial coinfection in patients with coronavirus disease 2019 (COVID-19) facilitates appropriate initiation or withholding of antibiotics. The Inflammatix Bacterial Viral Noninfected (IMX-BVN) classifier determines the likelihood of bacterial and viral infections. In a multicenter study, we investigated whether IMX-BVN version 3 (IMX-BVN-3) identifies patients with COVID-19 and bacterial coinfections or superinfections. Methods Patients with polymerase chain reaction-confirmed COVID-19 were enrolled in Berlin, Germany; Basel, Switzerland; and Cleveland, Ohio upon emergency department or hospital admission. PAXgene Blood RNA was extracted and 29 host mRNAs were quantified. IMX-BVN-3 categorized patients into very unlikely, unlikely, possible, and very likely bacterial and viral interpretation bands. IMX-BVN-3 results were compared with clinically adjudicated infection status. Results IMX-BVN-3 categorized 102 of 111 (91.9%) COVID-19 patients into very likely or possible, 7 (6.3%) into unlikely, and 2 (1.8%) into very unlikely viral bands. Approximately 94% of patients had IMX-BVN-3 unlikely or very unlikely bacterial results. Among 7 (6.3%) patients with possible (n = 4) or very likely (n = 3) bacterial results, 6 (85.7%) had clinically adjudicated bacterial coinfection or superinfection. Overall, 19 of 111 subjects for whom adjudication was performed had a bacterial infection; 7 of these showed a very likely or likely bacterial result in IMX-BVN-3. Conclusions IMX-BVN-3 identified COVID-19 patients as virally infected and identified bacterial coinfections and superinfections. Future studies will determine whether a point-of-care version of the classifier may improve the management of COVID-19 patients, including appropriate antibiotic use.
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Affiliation(s)
- Wolfgang Bauer
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Emergency Medicine, Berlin, Germany
| | - Sven Gläser
- Klinik für Innere Medizin–Pneumologie, Vivantes Klinikum Spandau und Klinik für Innere Medizin–Pneumologie und Infektiologie, Vivantes Klinikum Neukölln, Berlin, Germany
- Klinik für Innere Medizin–Pneumologie und Infektiologie, Vivantes Klinikum Neukölln, Berlin, Germany
| | - Dorina Thiemig
- Klinik für Innere Medizin–Pneumologie und Infektiologie, Vivantes Klinikum Neukölln, Berlin, Germany
| | - Katrin Wanner
- Klinik für Innere Medizin–Pneumologie, Vivantes Klinikum Spandau und Klinik für Innere Medizin–Pneumologie und Infektiologie, Vivantes Klinikum Neukölln, Berlin, Germany
| | - Alexander Peric
- Klinik für Innere Medizin–Pneumologie und Infektiologie, Vivantes Klinikum im Friedrichshain, Berlin, Germany
| | - Steffen Behrens
- Klinik für Innere Medizin–Kardiologie, Vivantes–Netzwerk für Gesundheit/Vivantes Humboldt-Klinikum and Klinik für Innere Medizin–Kardiologie und konservative Intensivmedizin, Vivantes–Netzwerk für Gesundheit/Vivantes Klinikum Spandau, Berlin, Germany
| | - Johanna Bialas
- Labor Berlin–Charité Vivantes Services GmbH, Berlin, Germany
| | - Angelika Behrens
- Klinik für Innere Medizin, Gastroenterologie und Pneumologie, Evangelische Elisabeth Klinik Krankenhausbetriebs gGmbH, Berlin, Germany
| | - Noa Galtung
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Emergency Medicine, Berlin, Germany
| | | | - Lisa Sun
- Inflammatix Inc, Burlingame, California, USA
| | - Larissa May
- Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, California, USA
| | - Sharron Mace
- Department of Emergency Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Sebastian Ott
- Department of Pulmonary Medicine, St Claraspital AG, Basel, Switzerland
- University of Bern, Bern, Switzerland
| | - Silvan Vesenbeckh
- Department of Pulmonary Medicine, St Claraspital AG, Basel, Switzerland
- Department of Pulmonology, University Hospital Zürich, Zürich, Switzerland
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Kostaki A, Wacker JW, Safarika A, Solomonidi N, Katsaros K, Giannikopoulos G, Koutelidakis IM, Hogan CA, Uhle F, Liesenfeld O, Sweeney TE, Giamarellos-Bourboulis EJ. A 29-MRNA HOST RESPONSE WHOLE-BLOOD SIGNATURE IMPROVES PREDICTION OF 28-DAY MORTALITY AND 7-DAY INTENSIVE CARE UNIT CARE IN ADULTS PRESENTING TO THE EMERGENCY DEPARTMENT WITH SUSPECTED ACUTE INFECTION AND/OR SEPSIS. Shock 2022; 58:224-230. [PMID: 36125356 PMCID: PMC9512237 DOI: 10.1097/shk.0000000000001970] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 03/28/2022] [Accepted: 07/19/2022] [Indexed: 11/25/2022]
Abstract
ABSTRACT Background: Risk stratification of emergency department patients with suspected acute infections and/or suspected sepsis remains challenging. We prospectively validated a 29-messenger RNA host response classifier for predicting severity in these patients. Methods: We enrolled adults presenting with suspected acute infections and at least one vital sign abnormality to six emergency departments in Greece. Twenty-nine target host RNAs were quantified on NanoString nCounter and analyzed with the Inflammatix Severity 2 (IMX-SEV-2) classifier to determine risk scores as low, moderate, and high severity. Performance of IMX-SEV-2 for prediction of 28-day mortality was compared with that of lactate, procalcitonin, and quick sequential organ failure assessment (qSOFA). Results: A total of 397 individuals were enrolled; 38 individuals (9.6%) died within 28 days. Inflammatix Severity 2 classifier predicted 28-day mortality with an area under the receiver operator characteristics curve of 0.82 (95% confidence interval [CI], 0.74-0.90) compared with lactate, 0.66 (95% CI, 0.54-0.77); procalcitonin, 0.67 (95% CI, 0.57-0.78); and qSOFA, 0.81 (95% CI, 0.72-0.89). Combining qSOFA with IMX-SEV-2 improved prognostic accuracy from 0.81 to 0.89 (95% CI, 0.82-0.96). The high-severity (rule-in) interpretation band of IMX-SEV-2 demonstrated 96.9% specificity for predicting 28-day mortality, whereas the low-severity (rule-out) band had a sensitivity of 78.9%. Similarly, IMX-SEV-2 alone accurately predicted the need for day-7 intensive care unit care and further boosted overall accuracy when combined with qSOFA. Conclusions: Inflammatix Severity 2 classifier predicted 28-day mortality and 7-day intensive care unit care with high accuracy and boosted the accuracy of clinical scores when used in combination.
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Affiliation(s)
- Antigone Kostaki
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Greece
| | | | - Asimina Safarika
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Greece
| | - Nicky Solomonidi
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Greece
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Brakenridge SC, Chen UI, Loftus T, Ungaro R, Dirain M, Kerr A, Zhong L, Bacher R, Starostik P, Ghita G, Midic U, Darden D, Fenner B, Wacker J, Efron PA, Liesenfeld O, Sweeney TE, Moldawer LL. Evaluation of a Multivalent Transcriptomic Metric for Diagnosing Surgical Sepsis and Estimating Mortality Among Critically Ill Patients. JAMA Netw Open 2022; 5:e2221520. [PMID: 35819783 PMCID: PMC9277492 DOI: 10.1001/jamanetworkopen.2022.21520] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/19/2022] [Indexed: 02/02/2023] Open
Abstract
Importance Rapid and accurate discrimination of sepsis and its potential severity currently require multiple assays with slow processing times that are often inconclusive in discerning sepsis from sterile inflammation. Objective To analyze a whole-blood, multivalent, host-messenger RNA expression metric for estimating the likelihood of bacterial infection and 30-day mortality and compare performance of the metric with that of other diagnostic and prognostic biomarkers and clinical parameters. Design, Setting, and Participants This prospective diagnostic and prognostic study was performed in the surgical intensive care unit (ICU) of a single, academic health science center. The analysis included 200 critically ill adult patients admitted with suspected sepsis (cohort A) or those at high risk for developing sepsis (cohort B) between July 1, 2020, and July 30, 2021. Exposures Whole-blood sample measurements of a custom 29-messenger RNA transcriptomic metric classifier for likelihood of bacterial infection (IMX-BVN-3) or 30-day mortality (severity) (IMX-SEV-3) in a clinical-diagnostic laboratory setting using an analysis platform (510[k]-cleared nCounter FLEX; NanoString, Inc), compared with measurement of procalcitonin and interleukin 6 (IL-6) plasma levels, and maximum 24-hour sequential organ failure assessment (SOFA) scores. Main Outcomes and Measures Estimated sepsis and 30-day mortality performance. Results Among the 200 patients included (124 men [62.0%] and 76 women [38.0%]; median age, 62.5 [IQR, 47.0-72.0] years), the IMX-BVN-3 bacterial infection classifier had an area under the receiver operating characteristics curve (AUROC) of 0.84 (95% CI, 0.77-0.90) for discriminating bacterial infection at ICU admission, similar to procalcitonin (0.85 [95% CI, 0.79-0.90]; P = .79) and significantly better than IL-6 (0.67 [95% CI, 0.58-0.75]; P < .001). For estimating 30-day mortality, the IMX-SEV-3 metric had an AUROC of 0.81 (95% CI, 0.66-0.95), which was significantly better than IL-6 levels (0.57 [95% CI, 0.37-0.77]; P = .006), marginally better than procalcitonin levels (0.65 [95% CI, 0.50-0.79]; P = .06), and similar to the SOFA score (0.76 [95% CI, 0.62-0.91]; P = .48). Combining IMX-BVN-3 and IMX-SEV-3 with procalcitonin or IL-6 levels or SOFA scores did not significantly improve performance. Among patients with sepsis, IMX-BVN-3 scores decreased over time, reflecting the resolution of sepsis. In 11 individuals at high risk (cohort B) who subsequently developed sepsis during their hospital course, IMX-BVN-3 bacterial infection scores did not decline over time and peaked on the day of documented infection. Conclusions and Relevance In this diagnostic and prognostic study, a novel, multivalent, transcriptomic metric accurately estimated the presence of bacterial infection and risk for 30-day mortality in patients admitted to a surgical ICU. The performance of this single transcriptomic metric was equivalent to or better than multiple alternative diagnostic and prognostic metrics when measured at admission and provided additional information when measured over time.
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Affiliation(s)
- Scott C. Brakenridge
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
- Division of Burn, Trauma & Critical Care Surgery, Department of Surgery, University of Washington, Seattle
| | - Uan-I Chen
- Inflammatix, Inc, Burlingame, California
| | - Tyler Loftus
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | - Ricardo Ungaro
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | - Marvin Dirain
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | - Austin Kerr
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | - Luer Zhong
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | - Rhonda Bacher
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | - Petr Starostik
- Molecular Pathology Laboratory at Rocky Point, Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville
- Clinical and Diagnostic Laboratories, Health Science Center, UF (University of Florida) Health Shands Hospital, Gainesville
| | - Gabriella Ghita
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | - Uros Midic
- Inflammatix, Inc, Burlingame, California
| | - Dijoia Darden
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | - Brittany Fenner
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | | | - Philip A. Efron
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
| | | | | | - Lyle L. Moldawer
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville
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STANCIOIU F, IVANESCU B, DUMITRESCU R. Perspectives on the Immune System in Sepsis. MAEDICA 2022; 17:404-414. [PMID: 36032596 PMCID: PMC9375866 DOI: 10.26574/maedica.2022.17.2.395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Beyond the modifications shown by the biochemistry labs, profound and ample modifications are seen in septic patients at a molecular level stemming from DNA translation and gene expression, manifested as unique profiles of mRNA (messenger), as well as non-coding, functional RNAs: miRNA (micro) and lncRNAs (long non-coding). Counteracting these modifications requires treatment with pleiotropic molecules and/or combination of molecules and opens the possibility of future treatments with arrays of siRNAs and/or specific panels of small molecules tailored for each patient subpopulation.
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Affiliation(s)
| | | | - Radu DUMITRESCU
- University of Bucharest, Medicover Hospital, Bucharest, Romania
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Bassetti S, Tschudin-Sutter S, Egli A, Osthoff M. Optimizing antibiotic therapies to reduce the risk of bacterial resistance. Eur J Intern Med 2022; 99:7-12. [PMID: 35074246 DOI: 10.1016/j.ejim.2022.01.029] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 01/10/2022] [Accepted: 01/17/2022] [Indexed: 01/02/2023]
Abstract
The incidence of infections caused by bacteria that are resistant to antibiotics is constantly increasing. In Europe alone, it has been estimated that each year about 33'000 deaths are attributable to such infections. One important driver of antimicrobial resistance is the use and abuse of antibiotics in human medicine. Inappropriate prescribing of antibiotics is still very frequent: up to 50% of all antimicrobials prescribed in humans might be unnecessary and several studies show that at least 50% of antibiotic treatments are inadequate, depending on the setting. Possible strategies to optimize antibiotic use in everyday clinical practice and to reduce the risk of inducing bacterial resistance include: the implementation of rapid microbiological diagnostics for identification and antimicrobial susceptibility testing, the use of inflammation markers to guide initiation and duration of therapies, the reduction of standard durations of antibiotic courses, the individualization of antibiotic therapies and dosing considering pharmacokinetics/pharmacodynamics targets, and avoiding antibiotic classes carrying a higher risk for induction of bacterial resistance. Importantly, measures to improve antibiotic prescribing and antibiotic stewardship programs should focus on facilitating clinical reasoning and improving prescribing environment in order to remove any barriers to good prescribing.
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Affiliation(s)
- Stefano Bassetti
- Division of Internal Medicine, University Hospital Basel and University of Basel, Switzerland; Department of Clinical Research, University Hospital Basel and University of Basel, Switzerland.
| | - Sarah Tschudin-Sutter
- Department of Clinical Research, University Hospital Basel and University of Basel, Switzerland; Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel and University of Basel, Switzerland
| | - Adrian Egli
- Division of Clinical Bacteriology and Mycology, University Hospital Basel and University of Basel, Switzerland; Department of Biomedicine, University Hospital Basel and University of Basel, Switzerland
| | - Michael Osthoff
- Division of Internal Medicine, University Hospital Basel and University of Basel, Switzerland; Department of Clinical Research, University Hospital Basel and University of Basel, Switzerland
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Ram-Mohan N, Rogers AJ, Blish CA, Nadeau KC, Zudock EJ, Kim D, Quinn JV, Sun L, Liesenfeld O, Yang S. Detection of bacterial co-infections and prediction of fatal outcomes in COVID-19 patients presenting to the emergency department using a 29 mRNA host response classifier. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.03.14.22272394. [PMID: 35313598 PMCID: PMC8936113 DOI: 10.1101/2022.03.14.22272394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Objective Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial co-infection, and determining illness severity since current practices require separate workflows. Here we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting SARS-CoV-2 infection, bacterial co-infections, and predicting clinical severity of COVID-19. Methods 161 patients with PCR-confirmed COVID-19 (52.2% female, median age 50.0 years, 51% hospitalized, 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene Blood RNA) and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. Results The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrolment and the remaining oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial co-infection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e. Clostridioides difficile colitis (n=1), urinary tract infection (n=1), and clinically diagnosed bacterial infections (n=3) for a specificity of 99.4%. 2/101 (2.8%) patients in the IMX-SEV-3 Low and 7/60 (11.7%) in the Moderate severity classifications died within thirty days of enrollment. Conclusions IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19, bacterial co-infections, and predicted patients’ risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management including more accurate treatment decisions and optimized resource utilization.
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Affiliation(s)
- Nikhil Ram-Mohan
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Angela J. Rogers
- Department of Medicine-Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Catherine A. Blish
- Department of Medicine/Infectious Diseases, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Kari C. Nadeau
- Department of Medicine-Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Elizabeth J Zudock
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - David Kim
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - James V. Quinn
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | | | | | | | - Samuel Yang
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
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22
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Atallah J, Mansour MK. Implications of Using Host Response-Based Molecular Diagnostics on the Management of Bacterial and Viral Infections: A Review. Front Med (Lausanne) 2022; 9:805107. [PMID: 35186993 PMCID: PMC8850635 DOI: 10.3389/fmed.2022.805107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/03/2022] [Indexed: 12/15/2022] Open
Abstract
Host-based diagnostics are a rapidly evolving field that may serve as an alternative to traditional pathogen-based diagnostics for infectious diseases. Understanding the exact mechanisms underlying a host-immune response and deriving specific host-response signatures, biomarkers and gene transcripts will potentially achieve improved diagnostics that will ultimately translate to better patient outcomes. Several studies have focused on novel techniques and assays focused on immunodiagnostics. In this review, we will highlight recent publications on the current use of host-based diagnostics alone or in combination with traditional microbiological assays and their potential future implications on the diagnosis and prognostic accuracy for the patient with infectious complications. Finally, we will address the cost-effectiveness implications from a healthcare and public health perspective.
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Affiliation(s)
- Johnny Atallah
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Michael K Mansour
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
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23
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A Transcriptomic Severity Metric That Predicts Clinical Outcomes in Critically Ill Surgical Sepsis Patients. Crit Care Explor 2021; 3:e0554. [PMID: 34671746 PMCID: PMC8522866 DOI: 10.1097/cce.0000000000000554] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Supplemental Digital Content is available in the text. Clinically deployable methods for the rapid and accurate prediction of sepsis severity that could elicit a meaningful change in clinical practice are currently lacking. We evaluated a whole-blood, multiplex host-messenger RNA expression metric, Inflammatix-Severity-2, for identifying septic, hospitalized patients’ likelihood of 30-day mortality, development of chronic critical illness, discharge disposition, and/or secondary infections.
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