1
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Hao C, Hao R, Zhao H, Zhang Y, Sheng M, An Y. Identification and validation of sepsis subphenotypes using time-series data. Heliyon 2024; 10:e28520. [PMID: 38689952 PMCID: PMC11059505 DOI: 10.1016/j.heliyon.2024.e28520] [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: 08/25/2023] [Revised: 03/10/2024] [Accepted: 03/20/2024] [Indexed: 05/02/2024] Open
Abstract
Purpose The recognition of sepsis as a heterogeneous syndrome necessitates identifying distinct subphenotypes to select targeted treatment. Methods Patients with sepsis from the MIMIC-IV database (2008-2019) were randomly divided into a development cohort (80%) and an internal validation cohort (20%). Patients with sepsis from the ICU database of Peking University People's Hospital (2008-2022) were included in the external validation cohort. Time-series k-means clustering analysis and dynamic time warping was performed to develop and validate sepsis subphenotypes by analyzing the trends of 21 vital signs and laboratory indicators within 24 h after sepsis onset. Inflammatory biomarkers were compared in the ICU database of Peking University People's Hospital, whereas treatment heterogeneity was compared in the MIMIC-IV database. Findings Three sub-phenotypes were identified in the development cohort. Type A patients (N = 2525, 47%) exhibited stable vital signs and fair organ function, type B (N = 1552, 29%) was exhibited an obvious inflammatory response and stable organ function, and type C (N = 1251, 24%) exhibited severely impaired organ function with a deteriorating tendency. Type C demonstrated the highest mortality rate (33%) and levels of inflammatory biomarkers, followed by type B (24%), whereas type A exhibited the lowest mortality rate (11%) and levels of inflammatory biomarkers. These subphenotypes were confirmed in both the internal and external cohorts, demonstrating similar features and comparable mortality rates. In type C patients, survivors had significantly lower fluid intake within 24 h after sepsis onset (median 2891 mL, interquartile range (IQR) 1530-5470 mL) than that in non-survivors (median 4342 mL, IQR 2189-7305 mL). For types B and C, survivors showed a higher proportion of indwelling central venous catheters (p < 0.05). Conclusion Three novel phenotypes of patients with sepsis were identified and validated using time-series data, revealing significant heterogeneity in inflammatory biomarkers, treatments, and consistency across cohorts.
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Affiliation(s)
- Chenxiao Hao
- Department of Critical Care Medicine, Peking University People's Hospital, Beijing, 100044, China
| | - Rui Hao
- School of Computer Science, Beijing University of Posts and Telecommunications, Haidian District, Beijing, 100876, China
| | - Huiying Zhao
- Department of Critical Care Medicine, Peking University People's Hospital, Beijing, 100044, China
| | - Yong Zhang
- BNRist, DCST, RIIT, Tsinghua University, Beijing, 100084, China
| | - Ming Sheng
- BNRist, DCST, RIIT, Tsinghua University, Beijing, 100084, China
| | - Youzhong An
- Department of Critical Care Medicine, Peking University People's Hospital, Beijing, 100044, China
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2
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Cajander S, Kox M, Scicluna BP, Weigand MA, Mora RA, Flohé SB, Martin-Loeches I, Lachmann G, Girardis M, Garcia-Salido A, Brunkhorst FM, Bauer M, Torres A, Cossarizza A, Monneret G, Cavaillon JM, Shankar-Hari M, Giamarellos-Bourboulis EJ, Winkler MS, Skirecki T, Osuchowski M, Rubio I, Bermejo-Martin JF, Schefold JC, Venet F. Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine. THE LANCET. RESPIRATORY MEDICINE 2024; 12:305-322. [PMID: 38142698 DOI: 10.1016/s2213-2600(23)00330-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 08/14/2023] [Accepted: 08/24/2023] [Indexed: 12/26/2023]
Abstract
Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.
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Affiliation(s)
- Sara Cajander
- Department of Infectious Diseases, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Matthijs Kox
- Department of Intensive Care Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Brendon P Scicluna
- Department of Applied Biomedical Science, Faculty of Health Sciences, Mater Dei hospital, University of Malta, Msida, Malta; Centre for Molecular Medicine and Biobanking, University of Malta, Msida, Malta
| | - Markus A Weigand
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Raquel Almansa Mora
- Department of Cell Biology, Genetics, Histology and Pharmacology, University of Valladolid, Valladolid, Spain
| | - Stefanie B Flohé
- Department of Trauma, Hand, and Reconstructive Surgery, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Ignacio Martin-Loeches
- St James's Hospital, Dublin, Ireland; Hospital Clinic, Institut D'Investigacions Biomediques August Pi i Sunyer, Universidad de Barcelona, Barcelona, Spain
| | - Gunnar Lachmann
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany
| | - Massimo Girardis
- Department of Intensive Care and Anesthesiology, University Hospital of Modena, Modena, Italy
| | - Alberto Garcia-Salido
- Hospital Infantil Universitario Niño Jesús, Pediatric Critical Care Unit, Madrid, Spain
| | - Frank M Brunkhorst
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany
| | - Michael Bauer
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany; Integrated Research and Treatment Center, Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | - Antoni Torres
- Pulmonology Department. Hospital Clinic of Barcelona, University of Barcelona, Ciberes, IDIBAPS, ICREA, Barcelona, Spain
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Guillaume Monneret
- Immunology Laboratory, Hôpital E Herriot - Hospices Civils de Lyon, Lyon, France; Université Claude Bernard Lyon-1, Hôpital E Herriot, Lyon, France
| | | | - Manu Shankar-Hari
- Centre for Inflammation Research, Institute of Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
| | | | - Martin Sebastian Winkler
- Department of Anesthesiology and Intensive Care, Universitätsmedizin Göttingen, Göttingen, Germany
| | - Tomasz Skirecki
- Department of Translational Immunology and Experimental Intensive Care, Centre of Postgraduate Medical Education, Warsaw, Poland
| | - Marcin Osuchowski
- Ludwig Boltzmann Institute for Traumatology, The Research Center in Cooperation with AUVA, Vienna, Austria
| | - Ignacio Rubio
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany; Integrated Research and Treatment Center, Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | - Jesus F Bermejo-Martin
- Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain; School of Medicine, Universidad de Salamanca, Salamanca, Spain; Centro de Investigación Biomédica en Red en Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Joerg C Schefold
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Fabienne Venet
- Immunology Laboratory, Hôpital E Herriot - Hospices Civils de Lyon, Lyon, France; Centre International de Recherche en Infectiologie, Inserm U1111, CNRS, UMR5308, Ecole Normale Supeérieure de Lyon, Universiteé Claude Bernard-Lyon 1, Lyon, France.
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3
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van Amstel RBE, Kennedy JN, Scicluna BP, Bos LDJ, Peters-Sengers H, Butler JM, Cano-Gamez E, Knight JC, Vlaar APJ, Cremer OL, Angus DC, van der Poll T, Seymour CW, van Vught LA. Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes. Intensive Care Med 2023; 49:1360-1369. [PMID: 37851064 PMCID: PMC10622359 DOI: 10.1007/s00134-023-07239-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies. METHODS This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, β, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles. RESULTS All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made. CONCLUSION Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.
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Affiliation(s)
- Rombout B E van Amstel
- Department of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
- Laboratory of Experimental Intensive Care and Anesthesiology (L.E.I.C.A.), Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.
| | - Jason N Kennedy
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brendon P Scicluna
- Department of Applied Biomedical Science, Faculty of Health Sciences, Mater Dei Hospital, University of Malta, Msida, Malta
- Centre for Molecular Medicine and Biobanking, University of Malta, Msida, Malta
| | - Lieuwe D J Bos
- Department of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Laboratory of Experimental Intensive Care and Anesthesiology (L.E.I.C.A.), Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands
| | - Hessel Peters-Sengers
- Center for Experimental and Molecular Medicine, Amsterdam Infection and Immunity, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Joe M Butler
- Center for Experimental and Molecular Medicine, Amsterdam Infection and Immunity, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands
| | - Eddie Cano-Gamez
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Julian C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Alexander P J Vlaar
- Department of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Laboratory of Experimental Intensive Care and Anesthesiology (L.E.I.C.A.), Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands
| | - Olaf L Cremer
- Department of Intensive Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Derek C Angus
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tom van der Poll
- Center for Experimental and Molecular Medicine, Amsterdam Infection and Immunity, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands
- Division of Infectious Diseases, Department of Internal Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands
| | | | - Lonneke A van Vught
- Department of Intensive Care Medicine, Amsterdam UMC, Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Center for Experimental and Molecular Medicine, Amsterdam Infection and Immunity, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands
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4
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Agnello L, Ciaccio AM, Vidali M, Cortegiani A, Biundo G, Gambino CM, Scazzone C, Lo Sasso B, Ciaccio M. Monocyte distribution width (MDW) in sepsis. Clin Chim Acta 2023; 548:117511. [PMID: 37562521 DOI: 10.1016/j.cca.2023.117511] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/07/2023] [Accepted: 08/07/2023] [Indexed: 08/12/2023]
Abstract
Sepsis is a life-threatening syndrome due to a dysregulated host response to infection, which can be caused by bacterial, viral, or fungal infection. Thus, it is crucial to know how the different microorganisms influence the levels of a biomarker. In the last decade, monocyte distribution width (MDW) has emerged as a promising sepsis biomarker, especially in acute settings, such as the Emergency Department and Intensive Care Unit. In this article, we explore the relationship between MDW and the different pathogens causing infection. Noteworthy, MDW is not a biological molecule, but it is calculated by a mathematical formula based on monocyte characteristics. Monocytes represent the first line defence against microorganisms and undergo activation upon infection, independently from the invading pathogen. According to the knowledge on the biomarker biology and the few literatures evidence, MDW may be considered a biomarker of sepsis, independent of the causative pathogen. However, further investigations are warranted before drawing definite conclusion.
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Affiliation(s)
- Luisa Agnello
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Anna Maria Ciaccio
- Internal Medicine and Medical Specialties "G. D'Alessandro", Department of Health Promotion, Maternal and Infant Care, University of Palermo, Palermo, Italy
| | - Matteo Vidali
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Andrea Cortegiani
- Department of Surgical, Oncological and Oral Science, University of Palermo, Palermo, Italy
| | - Giuseppe Biundo
- Department of Laboratory Medicine, University Hospital "P. Giaccone", Palermo, Italy
| | - Caterina Maria Gambino
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy; Department of Laboratory Medicine, University Hospital "P. Giaccone", Palermo, Italy
| | - Concetta Scazzone
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Bruna Lo Sasso
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy; Department of Laboratory Medicine, University Hospital "P. Giaccone", Palermo, Italy
| | - Marcello Ciaccio
- Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy; Department of Laboratory Medicine, University Hospital "P. Giaccone", Palermo, Italy.
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5
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Balch JA, Chen UI, Liesenfeld O, Starostik P, Loftus TJ, Efron PA, Brakenridge SC, Sweeney TE, Moldawer LL. Defining critical illness using immunological endotypes in patients with and without sepsis: a cohort study. Crit Care 2023; 27:292. [PMID: 37474944 PMCID: PMC10360294 DOI: 10.1186/s13054-023-04571-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying immunological endotypes through gene expression patterns in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical intensive care unit (ICU) with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical ICU and at set intervals. METHODS We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020. Using an FDA-cleared analytical platform (nCounter FLEX®, NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause mortality, development of chronic critical illness, and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding. RESULTS Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive, though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p < 0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively). CONCLUSION Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.
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Affiliation(s)
- Jeremy A Balch
- Sepsis and Critical Illness Research Center, Department of Surgery, Shands Hospital, University of Florida College of Medicine, Room 6116, 1600 SW Archer Road, P. O. Box 100019, Gainesville, FL, 32610-0019, USA
| | - Uan-I Chen
- Inflammatix, Inc., Sunnyvale, CA, 94085, USA
| | | | - Petr Starostik
- UF Health Medical Laboratory at Rocky Point, Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Tyler J Loftus
- Sepsis and Critical Illness Research Center, Department of Surgery, Shands Hospital, University of Florida College of Medicine, Room 6116, 1600 SW Archer Road, P. O. Box 100019, Gainesville, FL, 32610-0019, USA
| | - Philip A Efron
- Sepsis and Critical Illness Research Center, Department of Surgery, Shands Hospital, University of Florida College of Medicine, Room 6116, 1600 SW Archer Road, P. O. Box 100019, Gainesville, FL, 32610-0019, USA
| | - Scott C Brakenridge
- Sepsis and Critical Illness Research Center, Department of Surgery, Shands Hospital, University of Florida College of Medicine, Room 6116, 1600 SW Archer Road, P. O. Box 100019, Gainesville, FL, 32610-0019, USA
- Department of Surgery, Harborview Medical Center, University of Washington School of Medicine, Seattle, WA, 63110, USA
| | | | - Lyle L Moldawer
- Sepsis and Critical Illness Research Center, Department of Surgery, Shands Hospital, University of Florida College of Medicine, Room 6116, 1600 SW Archer Road, P. O. Box 100019, Gainesville, FL, 32610-0019, USA.
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6
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Yang A, Kennedy JN, Reitz KM, Phillips G, Terry KM, Levy MM, Angus DC, Seymour CW. Time to treatment and mortality for clinical sepsis subtypes. Crit Care 2023; 27:236. [PMID: 37322546 PMCID: PMC10268363 DOI: 10.1186/s13054-023-04507-5] [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: 01/23/2023] [Accepted: 05/23/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND Sepsis is common, deadly, and heterogenous. Prior analyses of patients with sepsis and septic shock in New York State showed a risk-adjusted association between more rapid antibiotic administration and bundled care completion, but not an intravenous fluid bolus, with reduced in-hospital mortality. However, it is unknown if clinically identifiable sepsis subtypes modify these associations. METHODS Secondary analysis of patients with sepsis and septic shock enrolled in the New York State Department of Health cohort from January 1, 2015 to December 31, 2016. Patients were classified as clinical sepsis subtypes (α, β, γ, δ-types) using the Sepsis ENdotyping in Emergency CAre (SENECA) approach. Exposure variables included time to 3-h sepsis bundle completion, antibiotic administration, and intravenous fluid bolus completion. Then logistic regression models evaluated the interaction between exposures, clinical sepsis subtypes, and in-hospital mortality. RESULTS 55,169 hospitalizations from 155 hospitals were included (34% α, 30% β, 19% γ, 17% δ). The α-subtype had the lowest (N = 1,905, 10%) and δ-subtype had the highest (N = 3,776, 41%) in-hospital mortality. Each hour to completion of the 3-h bundle (aOR, 1.04 [95%CI, 1.02-1.05]) and antibiotic initiation (aOR, 1.03 [95%CI, 1.02-1.04]) was associated with increased risk-adjusted in-hospital mortality. The association differed across subtypes (p-interactions < 0.05). For example, the outcome association for the time to completion of the 3-h bundle was greater in the δ-subtype (aOR, 1.07 [95%CI, 1.05-1.10]) compared to α-subtype (aOR, 1.02 [95%CI, 0.99-1.04]). Time to intravenous fluid bolus completion was not associated with risk-adjusted in-hospital mortality (aOR, 0.99 [95%CI, 0.97-1.01]) and did not differ among subtypes (p-interaction = 0.41). CONCLUSION Timely completion of a 3-h sepsis bundle and antibiotic initiation was associated with reduced risk-adjusted in-hospital mortality, an association modified by clinically identifiable sepsis subtype.
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Affiliation(s)
- Anne Yang
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh Medical Center, PA, Pittsburgh, USA.
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA.
| | - Jason N Kennedy
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA
- Department of Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Katherine M Reitz
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA
- Department of Surgery, Division of Vascular Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Gary Phillips
- The Ohio State University, Center for Biostatistics, Columbus, OH, USA
| | | | - Mitchell M Levy
- Division of Pulmonary, Critical Care and Sleep Medicine, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Derek C Angus
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA
- Department of Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Christopher W Seymour
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA
- Department of Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Emergency Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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7
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Balch JA, Chen UI, Liesenfeld O, Starostik P, Loftus TJ, Efron PA, Brakenridge SC, Sweeney TE, Moldawer LL. Defining critical illness using immunological endotypes in patients with and without of sepsis: A cohort study. RESEARCH SQUARE 2023:rs.3.rs-2874506. [PMID: 37214996 PMCID: PMC10197751 DOI: 10.21203/rs.3.rs-2874506/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Background: Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying characteristic gene expression patterns, or endotypes, in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical ICU with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical intensive care unit (ICU) and at set intervals. Methods: We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020 . Using an FDA-cleared analytical platform (nCounter FLEX ® , NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause (in-hospital, 30-, 90-day) mortality, development of chronic critical illness (CCI), and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding. Results: Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive , though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p<0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively). Conclusion : Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.
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8
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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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