1
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Matson SM. Decoding Molecular Heterogeneity in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 2024; 210:380-381. [PMID: 38976853 PMCID: PMC11351803 DOI: 10.1164/rccm.202406-1208ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024] Open
Affiliation(s)
- Scott M Matson
- Division of Pulmonary, Critical Care and Sleep Medicine University of Kansas, Department of Medicine Kansas City, Kansas
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2
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Gordon AC, Alipanah-Lechner N, Bos LD, Dianti J, Diaz JV, Finfer S, Fujii T, Giamarellos-Bourboulis EJ, Goligher EC, Gong MN, Karakike E, Liu VX, Lumlertgul N, Marshall JC, Menon DK, Meyer NJ, Munroe ES, Myatra SN, Ostermann M, Prescott HC, Randolph AG, Schenck EJ, Seymour CW, Shankar-Hari M, Singer M, Smit MR, Tanaka A, Taccone FS, Thompson BT, Torres LK, van der Poll T, Vincent JL, Calfee CS. From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU. Am J Respir Crit Care Med 2024; 210:155-166. [PMID: 38687499 PMCID: PMC11273306 DOI: 10.1164/rccm.202311-2086so] [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: 11/14/2023] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.
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Affiliation(s)
| | - Narges Alipanah-Lechner
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California
| | | | - Jose Dianti
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
- Departamento de Cuidados Intensivos, Centro de Educación Médica e Investigaciones Clínicas, Buenos Aires, Argentina
| | | | - Simon Finfer
- School of Public Health, Imperial College London, London, United Kingdom
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Tomoko Fujii
- Jikei University School of Medicine, Jikei University Hospital, Tokyo, Japan
| | | | - Ewan C. Goligher
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Michelle Ng Gong
- Division of Critical Care Medicine and
- Division of Pulmonary Medicine, Department of Medicine and Department of Epidemiology and Population Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York
| | - Eleni Karakike
- Second Department of Critical Care Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Vincent X. Liu
- Division of Research, Kaiser Permanente, Oakland, California
| | - Nuttha Lumlertgul
- Excellence Center for Critical Care Nephrology, Division of Nephrology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - John C. Marshall
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
| | - David K. Menon
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nuala J. Meyer
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Elizabeth S. Munroe
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Sheila N. Myatra
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Marlies Ostermann
- King’s College London, Guy’s & St Thomas’ Hospital, London, United Kingdom
| | - Hallie C. Prescott
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
- Veterans Affairs Center for Clinical Management Research, Ann Arbor, Michigan
| | - Adrienne G. Randolph
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
- Department of Anaesthesia and
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
| | - Edward J. Schenck
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Christopher W. Seymour
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Manu Shankar-Hari
- Centre for Inflammation Research, Institute of Regeneration and Repair, University of Edinburgh, Edinburgh, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, Division of Medicine, University College London, London, United Kingdom
| | | | - Aiko Tanaka
- Department of Intensive Care, University of Fukui Hospital, Yoshida, Fukui, Japan
- Department of Anesthesiology and Intensive Care Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Fabio S. Taccone
- Department des Soins Intensifs, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles (ULB), Brussels, Belgium; and
| | - B. Taylor Thompson
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Lisa K. Torres
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Tom van der Poll
- Center of Experimental and Molecular Medicine, and
- Division of Infectious Diseases, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Jean-Louis Vincent
- Department des Soins Intensifs, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles (ULB), Brussels, Belgium; and
| | - Carolyn S. Calfee
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California
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3
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Kitsios GD, Sayed K, Fitch A, Yang H, Britton N, Shah F, Bain W, Evankovich JW, Qin S, Wang X, Li K, Patel A, Zhang Y, Radder J, Dela Cruz C, Okin DA, Huang CY, Van Tyne D, Benos PV, Methé B, Lai P, Morris A, McVerry BJ. Longitudinal multicompartment characterization of host-microbiota interactions in patients with acute respiratory failure. Nat Commun 2024; 15:4708. [PMID: 38830853 PMCID: PMC11148165 DOI: 10.1038/s41467-024-48819-8] [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: 09/18/2023] [Accepted: 05/13/2024] [Indexed: 06/05/2024] Open
Abstract
Critical illness can significantly alter the composition and function of the human microbiome, but few studies have examined these changes over time. Here, we conduct a comprehensive analysis of the oral, lung, and gut microbiota in 479 mechanically ventilated patients (223 females, 256 males) with acute respiratory failure. We use advanced DNA sequencing technologies, including Illumina amplicon sequencing (utilizing 16S and ITS rRNA genes for bacteria and fungi, respectively, in all sample types) and Nanopore metagenomics for lung microbiota. Our results reveal a progressive dysbiosis in all three body compartments, characterized by a reduction in microbial diversity, a decrease in beneficial anaerobes, and an increase in pathogens. We find that clinical factors, such as chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, are associated with specific patterns of dysbiosis. Interestingly, unsupervised clustering of lung microbiota diversity and composition by 16S independently predicted survival and performed better than traditional clinical and host-response predictors. These observations are validated in two separate cohorts of COVID-19 patients, highlighting the potential of lung microbiota as valuable prognostic biomarkers in critical care. Understanding these microbiome changes during critical illness points to new opportunities for microbiota-targeted precision medicine interventions.
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Affiliation(s)
- Georgios D Kitsios
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Khaled Sayed
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT, USA
| | - Adam Fitch
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Haopu Yang
- School of Medicine, Tsinghua University, Beijing, China
| | - Noel Britton
- Division of Pulmonary Critical Care Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MA, USA
| | - Faraaz Shah
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Veteran's Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - William Bain
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Veteran's Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - John W Evankovich
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shulin Qin
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xiaohong Wang
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kelvin Li
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Asha Patel
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yingze Zhang
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Josiah Radder
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Charles Dela Cruz
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel A Okin
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ching-Ying Huang
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Barbara Methé
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peggy Lai
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alison Morris
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bryan J McVerry
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
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Giamarellos-Bourboulis EJ, Aschenbrenner AC, Bauer M, Bock C, Calandra T, Gat-Viks I, Kyriazopoulou E, Lupse M, Monneret G, Pickkers P, Schultze JL, van der Poll T, van de Veerdonk FL, Vlaar APJ, Weis S, Wiersinga WJ, Netea MG. The pathophysiology of sepsis and precision-medicine-based immunotherapy. Nat Immunol 2024; 25:19-28. [PMID: 38168953 DOI: 10.1038/s41590-023-01660-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/21/2023] [Indexed: 01/05/2024]
Abstract
Sepsis remains a major cause of morbidity and mortality in both low- and high-income countries. Antibiotic therapy and supportive care have significantly improved survival following sepsis in the twentieth century, but further progress has been challenging. Immunotherapy trials for sepsis, mainly aimed at suppressing the immune response, from the 1990s and 2000s, have largely failed, in part owing to unresolved patient heterogeneity in the underlying immune disbalance. The past decade has brought the promise to break this blockade through technological developments based on omics-based technologies and systems medicine that can provide a much larger data space to describe in greater detail the immune endotypes in sepsis. Patient stratification opens new avenues towards precision medicine approaches that aim to apply immunotherapies to sepsis, on the basis of precise biomarkers and molecular mechanisms defining specific immune endotypes. This approach has the potential to lead to the establishment of immunotherapy as a successful pillar in the treatment of sepsis for future generations.
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Affiliation(s)
- Evangelos J Giamarellos-Bourboulis
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens Medical School, Athens, Greece
- Hellenic Institute for the Study of Sepsis, Athens, Greece
| | - Anna C Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Michael Bauer
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Friedrich-Schiller University, Jena, Germany
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Medical University of Vienna, Institute of Artificial Intelligence, Center for Medical Data Science, Vienna, Austria
| | - Thierry Calandra
- Service of Immunology and Allergy and Center of Human Immunology Lausanne, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Irit Gat-Viks
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Evdoxia Kyriazopoulou
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens Medical School, Athens, Greece
- Hellenic Institute for the Study of Sepsis, Athens, Greece
| | - Mihaela Lupse
- Infectious Diseases Hospital, University of Medicine and Pharmacy 'Iuliu Hatieganu', Cluj-Napoca, Romania
| | - Guillaume Monneret
- Joint Research Unit HCL-bioMérieux, EA 7426 'Pathophysiology of Injury-Induced Immunosuppression' (Université Claude Bernard Lyon 1 - Hospices Civils de Lyon, bioMérieux), Lyon, France
- Immunology Laboratory, Edouard Herriot Hospital - Hospices Civils de Lyon, Lyon, France
| | - Peter Pickkers
- Department of Intensive Care, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Joachim L Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Tom van der Poll
- Division of Infectious Diseases, Amsterdam University Medical Centers (Amsterdam UMC), Center for Experimental and Molecular Medicine (CEMM), University of Amsterdam, Amsterdam, The Netherlands
| | - Frank L van de Veerdonk
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Alexander P J Vlaar
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology L.E.C.A. Amsterdam Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Sebastian Weis
- Institute for Infectious Disease and Infection Control, Jena University Hospital, Friedrich-Schiller University, Jena, Germany
- Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute-HKI, Jena, Germany
| | - W Joost Wiersinga
- Division of Infectious Diseases, Amsterdam University Medical Centers (Amsterdam UMC), Center for Experimental and Molecular Medicine (CEMM), University of Amsterdam, Amsterdam, The Netherlands
| | - Mihai G Netea
- Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands.
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany.
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
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5
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Sanchez-Pinto LN, Bennett TD, Stroup EK, Luo Y, Atreya M, Bubeck Wardenburg J, Chong G, Geva A, Faustino EVS, Farris RW, Hall MW, Rogerson C, Shah SS, Weiss SL, Khemani RG. Derivation, Validation, and Clinical Relevance of a Pediatric Sepsis Phenotype With Persistent Hypoxemia, Encephalopathy, and Shock. Pediatr Crit Care Med 2023; 24:795-806. [PMID: 37272946 PMCID: PMC10540758 DOI: 10.1097/pcc.0000000000003292] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
OBJECTIVES Untangling the heterogeneity of sepsis in children and identifying clinically relevant phenotypes could lead to the development of targeted therapies. Our aim was to analyze the organ dysfunction trajectories of children with sepsis-associated multiple organ dysfunction syndrome (MODS) to identify reproducible and clinically relevant sepsis phenotypes and determine if they are associated with heterogeneity of treatment effect (HTE) to common therapies. DESIGN Multicenter observational cohort study. SETTING Thirteen PICUs in the United States. PATIENTS Patients admitted with suspected infections to the PICU between 2012 and 2018. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We used subgraph-augmented nonnegative matrix factorization to identify candidate trajectory-based phenotypes based on the type, severity, and progression of organ dysfunction in the first 72 hours. We analyzed the candidate phenotypes to determine reproducibility as well as prognostic, therapeutic, and biological relevance. Overall, 38,732 children had suspected infection, of which 15,246 (39.4%) had sepsis-associated MODS with an in-hospital mortality of 10.1%. We identified an organ dysfunction trajectory-based phenotype (which we termed persistent hypoxemia, encephalopathy, and shock) that was highly reproducible, had features of systemic inflammation and coagulopathy, and was independently associated with higher mortality. In a propensity score-matched analysis, patients with persistent hypoxemia, encephalopathy, and shock phenotype appeared to have HTE and benefit from adjuvant therapy with hydrocortisone and albumin. When compared with other high-risk clinical syndromes, the persistent hypoxemia, encephalopathy, and shock phenotype only overlapped with 50%-60% of patients with septic shock, moderate-to-severe pediatric acute respiratory distress syndrome, or those in the top tier of organ dysfunction burden, suggesting that it represents a nonsynonymous clinical phenotype of sepsis-associated MODS. CONCLUSIONS We derived and validated the persistent hypoxemia, encephalopathy, and shock phenotype, which is highly reproducible, clinically relevant, and associated with HTE to common adjuvant therapies in children with sepsis.
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Affiliation(s)
- L Nelson Sanchez-Pinto
- Department of Pediatrics, Northwestern University Feinberg School of Medicine and Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO
| | - Emily K Stroup
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Mihir Atreya
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | | | - Grace Chong
- Department of Pediatrics, University of Chicago Pritzker School of Medicine, Chicago, IL
| | - Alon Geva
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
- Department of Anaesthesia, Harvard Medical School, Boston, MA
| | | | - Reid W Farris
- Department of Pediatrics, University of Washington and Seattle Children's Hospital, Seattle, WA
| | - Mark W Hall
- Department of Pediatrics, The Ohio State University and Nationwide Children's Hospital, Columbus, OH
| | - Colin Rogerson
- Department of Pediatrics, Indiana University School of Medicine, Riley Hospital for Children, Indianapolis, IN
| | - Sareen S Shah
- Department of Pediatrics, Cohen Children's Medical Center, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New Hyde Park, NY
| | - Scott L Weiss
- Department of Anesthesiology and Critical Care, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Robinder G Khemani
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Los Angeles, Los Angeles, CA
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6
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Lyons PG, McEvoy CA, Hayes-Lattin B. Sepsis and acute respiratory failure in patients with cancer: how can we improve care and outcomes even further? Curr Opin Crit Care 2023; 29:472-483. [PMID: 37641516 PMCID: PMC11142388 DOI: 10.1097/mcc.0000000000001078] [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] [Indexed: 08/31/2023]
Abstract
PURPOSE OF REVIEW Care and outcomes of critically ill patients with cancer have improved over the past decade. This selective review will discuss recent updates in sepsis and acute respiratory failure among patients with cancer, with particular focus on important opportunities to improve outcomes further through attention to phenotyping, predictive analytics, and improved outcome measures. RECENT FINDINGS The prevalence of cancer diagnoses in intensive care units (ICUs) is nontrivial and increasing. Sepsis and acute respiratory failure remain the most common critical illness syndromes affecting these patients, although other complications are also frequent. Recent research in oncologic sepsis has described outcome variation - including ICU, hospital, and 28-day mortality - across different types of cancer (e.g., solid vs. hematologic malignancies) and different sepsis definitions (e.g., Sepsis-3 vs. prior definitions). Research in acute respiratory failure in oncology patients has highlighted continued uncertainty in the value of diagnostic bronchoscopy for some patients and in the optimal respiratory support strategy. For both of these syndromes, specific challenges include multifactorial heterogeneity (e.g. in etiology and/or underlying cancer), delayed recognition of clinical deterioration, and complex outcomes measurement. SUMMARY Improving outcomes in oncologic critical care requires attention to the heterogeneity of cancer diagnoses, timely recognition and management of critical illness, and defining appropriate ICU outcomes.
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Affiliation(s)
- Patrick G Lyons
- Department of Medicine, Oregon Health & Science University
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University
- Knight Cancer Institute, Oregon Health & Science University
| | - Colleen A McEvoy
- Department of Medicine, Washington University School of Medicine
- Siteman Cancer Center, Washington University School of Medicine
| | - Brandon Hayes-Lattin
- Department of Medicine, Oregon Health & Science University
- Knight Cancer Institute, Oregon Health & Science University
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7
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Kitsios GD, Sayed K, Fitch A, Yang H, Britton N, Shah F, Bain W, Evankovich JW, Qin S, Wang X, Li K, Patel A, Zhang Y, Radder J, Dela Cruz C, Okin DA, Huang CY, van Tyne D, Benos PV, Methé B, Lai P, Morris A, McVerry BJ. Prognostic Insights from Longitudinal Multicompartment Study of Host-Microbiota Interactions in Critically Ill Patients. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.25.23296086. [PMID: 37808745 PMCID: PMC10557814 DOI: 10.1101/2023.09.25.23296086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Critical illness can disrupt the composition and function of the microbiome, yet comprehensive longitudinal studies are lacking. We conducted a longitudinal analysis of oral, lung, and gut microbiota in a large cohort of 479 mechanically ventilated patients with acute respiratory failure. Progressive dysbiosis emerged in all three body compartments, characterized by reduced alpha diversity, depletion of obligate anaerobe bacteria, and pathogen enrichment. Clinical variables, including chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, shaped dysbiosis. Notably, of the three body compartments, unsupervised clusters of lung microbiota diversity and composition independently predicted survival, transcending clinical predictors, organ dysfunction severity, and host-response sub-phenotypes. These independent associations of lung microbiota may serve as valuable biomarkers for prognostication and treatment decisions in critically ill patients. Insights into the dynamics of the microbiome during critical illness highlight the potential for microbiota-targeted interventions in precision medicine.
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Affiliation(s)
- Georgios D. Kitsios
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Khaled Sayed
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT, USA
| | - Adam Fitch
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Haopu Yang
- School of Medicine, Tsinghua University, Beijing, China
| | - Noel Britton
- Division of Pulmonary Critical Care Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Faraaz Shah
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Veteran’s Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - William Bain
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Veteran’s Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - John W. Evankovich
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shulin Qin
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xiaohong Wang
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kelvin Li
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Asha Patel
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yingze Zhang
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Josiah Radder
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Charles Dela Cruz
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel A Okin
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ching-Ying Huang
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Daria van Tyne
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Barbara Methé
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peggy Lai
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alison Morris
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bryan J. McVerry
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
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8
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Kitsios GD, Sayed K, Fitch A, Yang H, Britton N, Shah F, Bain W, Evankovich JW, Qin S, Wang X, Li K, Patel A, Zhang Y, Radder J, Cruz CD, Okin DA, Huang CY, van Tyne D, Benos PV, Methé B, Lai P, Morris A, McVerry BJ. Prognostic Insights from Longitudinal Multicompartment Study of Host-Microbiota Interactions in Critically Ill Patients. RESEARCH SQUARE 2023:rs.3.rs-3338762. [PMID: 37841841 PMCID: PMC10571606 DOI: 10.21203/rs.3.rs-3338762/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Critical illness can disrupt the composition and function of the microbiome, yet comprehensive longitudinal studies are lacking. We conducted a longitudinal analysis of oral, lung, and gut microbiota in a large cohort of 479 mechanically ventilated patients with acute respiratory failure. Progressive dysbiosis emerged in all three body compartments, characterized by reduced alpha diversity, depletion of obligate anaerobe bacteria, and pathogen enrichment. Clinical variables, including chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, shaped dysbiosis. Notably, of the three body compartments, unsupervised clusters of lung microbiota diversity and composition independently predicted survival, transcending clinical predictors, organ dysfunction severity, and host-response sub-phenotypes. These independent associations of lung microbiota may serve as valuable biomarkers for prognostication and treatment decisions in critically ill patients. Insights into the dynamics of the microbiome during critical illness highlight the potential for microbiota-targeted interventions in precision medicine.
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Affiliation(s)
- Georgios D. Kitsios
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Khaled Sayed
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT, USA
| | - Adam Fitch
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Haopu Yang
- School of Medicine, Tsinghua University, Beijing, China
| | - Noel Britton
- Division of Pulmonary Critical Care Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Faraaz Shah
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Veteran’s Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - William Bain
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Veteran’s Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - John W. Evankovich
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shulin Qin
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xiaohong Wang
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kelvin Li
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Asha Patel
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yingze Zhang
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Josiah Radder
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Charles Dela Cruz
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel A Okin
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ching-Ying Huang
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Daria van Tyne
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Barbara Methé
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peggy Lai
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alison Morris
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bryan J. McVerry
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, PA, USA
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9
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Sikora A, Jeong H, Yu M, Chen X, Murray B, Kamaleswaran R. Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients. Sci Rep 2023; 13:15562. [PMID: 37730817 PMCID: PMC10511715 DOI: 10.1038/s41598-023-42657-2] [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: 06/10/2022] [Accepted: 09/13/2023] [Indexed: 09/22/2023] Open
Abstract
Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5 to 9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA, USA.
| | | | - Mengyun Yu
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, USA
| | - Xianyan Chen
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, USA
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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10
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Papathanakos G, Andrianopoulos I, Xenikakis M, Papathanasiou A, Koulenti D, Blot S, Koulouras V. Clinical Sepsis Phenotypes in Critically Ill Patients. Microorganisms 2023; 11:2165. [PMID: 37764009 PMCID: PMC10538192 DOI: 10.3390/microorganisms11092165] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/10/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.
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Affiliation(s)
- Georgios Papathanakos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Ioannis Andrianopoulos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Menelaos Xenikakis
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Athanasios Papathanasiou
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Despoina Koulenti
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QL 4029, Australia;
- Second Critical Care Department, Attikon University Hospital, Rimini Street, 12462 Athens, Greece
| | - Stijn Blot
- Department of Internal Medicine & Pediatrics, Ghent University, 9000 Ghent, Belgium;
| | - Vasilios Koulouras
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
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11
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Motes A, Singh T, Vinan Vega N, Nugent K. A Focused Review of the Initial Management of Patients with Acute Respiratory Distress Syndrome. J Clin Med 2023; 12:4650. [PMID: 37510765 PMCID: PMC10380732 DOI: 10.3390/jcm12144650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/24/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
At present, the management of patients with acute respiratory distress syndrome (ARDS) largely focuses on ventilator settings to limit intrathoracic pressures by using low tidal volumes and on FiO2/PEEP relationships to maintain optimal gas exchange. Acute respiratory distress syndrome is a complex medical disorder that can develop in several primary acute disorders, has a rapid time course, and has several classifications that can reflect either the degree of hypoxemia, the extent of radiographic involvement, or the underlying pathogenesis. The identification of subtypes of patients with ARDS would potentially make precision medicine possible in these patients. This is a very difficult challenge given the heterogeneity in the clinical presentation, pathogenesis, and treatment responses in these patients. The analysis of large databases of patients with acute respiratory failure using statistical methods such as cluster analysis could identify phenotypes that have different outcomes or treatment strategies. However, clinical information available on presentation is unlikely to separate patients into groups that allow for secure treatment decisions or outcome predictions. In some patients, non-invasive positive pressure ventilation provides adequate support through episodes of acute respiratory failure, and the development of specialized units to manage patients with this support might lead to the better use of hospital resources. Patients with ARDS have capillary leak, which results in interstitial and alveolar edema. Early attention to fluid balance in these patients might improve gas exchange and alter the pathophysiology underlying the development of severe ARDS. Finally, more attention to the interaction of patients with ventilators through complex monitoring systems has the potential to identify ventilator dyssynchrony, leading to ventilator adjustments and potentially better outcomes. Recent studies with COVID-19 patients provide tentative answers to some of these questions. In addition, expert clinical investigators have analyzed the promise and difficulties associated with the development of precision medicine in patients with ARDS.
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Affiliation(s)
- Arunee Motes
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Tushi Singh
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Noella Vinan Vega
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Kenneth Nugent
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
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12
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Tenda ED, Henrina J, Samosir J, Amalia R, Yulianti M, Pitoyo CW, Setiati S. Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review. Heliyon 2023; 9:e17276. [PMID: 37366530 PMCID: PMC10275654 DOI: 10.1016/j.heliyon.2023.e17276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 06/02/2023] [Accepted: 06/13/2023] [Indexed: 06/28/2023] Open
Abstract
COVID-19-related acute respiratory distress syndrome (CARDS) has been suggested to differ from the typical ARDS. While distinct phenotypes of ARDS have been identified through latent class analysis (LCA), it is unclear whether such phenotypes exist for CARDS and how they affect clinical outcomes. To address this question, we conducted a systematic review of the current evidence.We searched several, including PubMed, EBSCO Host, and Web of Science, from inception to July 1, 2022. Our exposure and outcome of interest were different CARDS phenotypes identified and their associated outcomes, such as 28-day, 90-day, 180-day mortality, ventilator-free days, and other relevant outcomes.We identified four studies comprising a total of 1776 CARDS patients.Of the four studies, three used LCA to identify subphenotypes (SPs) of CARDS. One study based on longitudinal data identified two SPs, with SP2 associated with worse ventilation and mechanical parameters than SP1. The other two studies based on baseline data also identified two SPs, with SP2 and SP1 were associated with hyperinflammatory and hypoinflammatory CARDS, respectively. The fourth study identified three SPs primarily stratified by comorbidities using multifactorial analysis.All studies identified a subphenotype associated with poorer outcomes, including mortality, ventilator-free days, multiple-organ injury, and pulmonary embolism. Two studies reported differential responses to corticosteroids among the SPs, with improved mortality in the hyperinflammatory and worse in the hypoinflammatory SPs.Overall, our review highlights the importance of phenotyping in understanding CARDS and its impact on disease management and prognostication. However, a consensus approach to phenotyping is necessary to ensure consistency and comparability across studies. We recommend that randomized clinical trials stratified by phenotype should only be initiated after such consensus is reached. Short title COVID-19 ARDS subphenotypes and outcomes.
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Affiliation(s)
- Eric Daniel Tenda
- Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Joshua Henrina
- Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Jistrani Samosir
- Department of Internal Medicine, Dr.T.C. Hillers General Public Hospital, East Nusa Tenggara, Kabupaten Sikka, Indonesia
| | - Ridha Amalia
- University of Indonesia Hospital, Depok, Indonesia
| | - Mira Yulianti
- Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Ceva Wicaksono Pitoyo
- Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Siti Setiati
- Division of Geriatric Medicine, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia
- Clinical Epidemiology and Evidence-Based Medicine Unit, Faculty of Medicine, Cipto Mangunkusumo Hospital, Universitas Indonesia, Jakarta, Indonesia
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13
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Zampieri FG, Damiani LP, Bagshaw SM, Semler MW, Churpek M, Azevedo LCP, Figueiredo RC, Veiga VC, Biondi R, Freitas FR, Machado FR, Cavalcanti AB. Conditional Treatment Effect Analysis of Two Infusion Rates for Fluid Challenges in Critically Ill Patients: A Secondary Analysis of Balanced Solution versus Saline in Intensive Care Study (BaSICS) Trial. Ann Am Thorac Soc 2023; 20:872-879. [PMID: 36735931 PMCID: PMC10257031 DOI: 10.1513/annalsats.202211-946oc] [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: 11/15/2022] [Accepted: 02/03/2023] [Indexed: 02/05/2023] Open
Abstract
Rationale: Optimal infusion rate for fluid challenges in critically ill patients is unknown. A large clinical trial comparing two different infusion rates yielded neutral results. Conditional average treatment effect (CATE) assessment may aid in tailoring therapy. Objectives: To estimate CATE in patients enrolled in the BaSICS trial and to assess the effects of receiving CATE model-recommended treatment in terms of hospital mortality. Methods: Post hoc analysis of the BaSICS trial assessing the effect of two infusion rates for the fluid challenge (fast, 999 ml/h, control group; vs. slow, 333 ml/h, intervention group) on hospital mortality. CATE was estimated as the difference in outcome for treatment arms in counterfactuals obtained from a Bayesian model trained in the first half of the trial adjusted for predictors hypothesized to interact with the intervention. The model recommended slow or fast infusion or made no recommendation in the second half. A threshold greater than 0.90 probability of benefit was considered. Results: A total of 10,465 patients were analyzed. The model was trained in 5,230 patients and tested in 5,235 patients. A recommendation could be made in the test set in 19% of patients (14% were recommended the control group and 5% the treatment group); for 81% of patients, no recommendation could be made. Slow infusion was more frequently recommended in cases of planned admissions in younger patients; fast infusion was recommended for older patients with sepsis. Slow infusion rate in the subgroup of patients in the test set in which slow infusion was recommended by the model was associated with an odds ratio of 0.58 (95% credible interval of 0.32-0.90; 0.99 posterior probability of benefit) for hospital mortality. Fast infusion in the subgroup in which the model recommended fast infusion was associated with an odds ratio of 0.72 (credible intervals from 0.54 to 0.91; probability of benefit >0.99). Conclusions: Estimation of CATEs from counterfactual probabilities in data from BaSICS provided additional information on trial data. Agreement between treatment recommendation and actual treatment was associated with lower hospital mortality. Clinical trial registered with clinicaltrials.gov (NCT02875873).
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Affiliation(s)
- Fernando G Zampieri
- Hospital do Coracao (HCor)-Research Institute, São Paulo, Brazil
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
- Alberta Health Services, Edmonton, Alberta, Canada
| | - Lucas P Damiani
- Hospital do Coracao (HCor)-Research Institute, São Paulo, Brazil
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
- Alberta Health Services, Edmonton, Alberta, Canada
| | - Matthew W Semler
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Matthew Churpek
- Division of Allergy, Pulmonary, and Critical Care, Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Rodrigo C Figueiredo
- Hospital Maternidade São José, Centro Universitário do Espírito Santo, Colatina, Brazil
| | - Viviane C Veiga
- BP - A Beneficência Portuguesa de São Paulo, São Paulo, Brazil
| | - Rodrigo Biondi
- Instituto de Cardiologia do Distrito Federal, Brasília, Brazil
| | | | - Flavia R Machado
- Department of Anesthesiology, Pain and Intensive Care, Universidade Federal de São Paulo, São Paulo, Brazil
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Sikora A, Rafiei A, Rad MG, Keats K, Smith SE, Devlin JW, Murphy DJ, Murray B, Kamaleswaran R. Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model. Crit Care 2023; 27:167. [PMID: 37131200 PMCID: PMC10155304 DOI: 10.1186/s13054-023-04437-2] [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: 03/13/2023] [Accepted: 04/10/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed 'pharmacophenotypes') correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality). METHODS This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate. RESULTS A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay (p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6. CONCLUSION The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA USA
| | - Alireza Rafiei
- Department of Computer Science and Informatics, Emory University, Atlanta, GA USA
| | - Milad Ghiasi Rad
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA USA
| | - Kelli Keats
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA USA
| | - Susan E. Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA USA
| | - John W. Devlin
- Northeastern University School of Pharmacy, Boston, MA USA
- Brigham and Women’s Hospital, Division of Pulmonary and Critical Care Medicine, Boston, MA USA
| | - David J. Murphy
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA USA
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA USA
| | - MRC-ICU Investigator Team
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA USA
- Department of Computer Science and Informatics, Emory University, Atlanta, GA USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA USA
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA USA
- Northeastern University School of Pharmacy, Boston, MA USA
- Brigham and Women’s Hospital, Division of Pulmonary and Critical Care Medicine, Boston, MA USA
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA USA
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA USA
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15
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Liu Z, Li Y, Zhao Q, Kang Y. Association and predictive value of soluble thrombomodulin with mortality in patients with acute respiratory distress syndrome: systematic review and meta-analysis. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:181. [PMID: 36923081 PMCID: PMC10009569 DOI: 10.21037/atm-23-432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023]
Abstract
Background Acute respiratory distress syndrome (ARDS) is a heterogeneous illness that has a high mortality rate. The role and predictive value of soluble thrombomodulin (sTM) in ARDS mortality is disputable, so the present study aimed to evaluate the association and predictive value of sTM for the in-hospital mortality of ARDS. Methods PubMed, Web of Science, Embase, Cochrane Library, Chongqing VIP, WanFang, China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature databases were searched for relevant literature published before October 10, 2022. Relevant observable studies were included for analysis. The Newcastle-Ottawa Scale and QUAPAS (Quality Assessment of Prognostic Accuracy Studies) were employed to appraise the quality of the included studies. Results Thirteen articles were included in the present study. The eligible studies were of moderate to high quality [Newcastle-Ottawa Scale (NOS) 5-8 scores], and the high risk of bias in the included studieson predictive value was mainly distributed in participant and analysis domains of QUAPAS. There were 1,992 patients with ARDS, and 538 died. Our meta-analysis demonstrated that nonsurvivors had more significantly increased sTM levels than did survivors [standardized mean difference (SMD) =1.473; 95% CI: 0.874-2.072; P<0.001]. Elevated sTM levels had an independent correlation with higher mortality in patients with ARDS [pooled odds ratio (OR) =2.126; 95% CI: 1.548-2.920; P<0.001]. sTM showed satisfactory performance in predicting the mortality of ARDS [summary receiver operating characteristic curve (SROC) =0.78; 95% CI: 0.64-0.89]. The pooled sensitivity was 72% (95% CI: 66-77%), and the pooled specificity was 77% (95% CI: 72-82%). Subgroup analysis showed no significant difference in the sTM levels between nonsurvivors and survivors in terms of patients with direct ARDS (SMD =0.813; 95% CI: -0.673 to 2.229; P=0.253). Conclusions sTM is associated with hospital mortality in ARDS and shows moderate predictive performance. As a result, it is a potential candidate for predicting the mortality of ARDS. However, caution is needed when sTM is used to predict adverse outcomes in patients with direct ARDS.
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Affiliation(s)
- Zhenjun Liu
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.,Department of Critical Care Medicine, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi Li
- Department of Critical Care Medicine, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Zhao
- Department of Critical Care Medicine, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Kang
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
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16
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Lessons From a Negative Clinical Trial: Novel Immunological Targets for COVID-19 and Beyond. Crit Care Med 2023; 51:153-156. [PMID: 36519993 PMCID: PMC9749943 DOI: 10.1097/ccm.0000000000005719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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17
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Ruscic K, Hanidziar D, Shaw K, Wiener-Kronish J, Shelton KT. Systems Anesthesiology: Integrating Insights From Diverse Disciplines to Improve Perioperative Care. Anesth Analg 2022; 135:673-677. [PMID: 36108178 PMCID: PMC9494922 DOI: 10.1213/ane.0000000000006166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Katarina Ruscic
- Division of Critical Care, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Dusan Hanidziar
- Division of Critical Care, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Kendrick Shaw
- Division of Critical Care, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Jeanine Wiener-Kronish
- Division of Critical Care, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Kenneth T Shelton
- Division of Critical Care, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA
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More questions than answers for the use of inhaled nitric oxide in COVID-19. Nitric Oxide 2022; 124:39-48. [PMID: 35526702 PMCID: PMC9072755 DOI: 10.1016/j.niox.2022.05.001] [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: 02/25/2022] [Revised: 04/08/2022] [Accepted: 05/03/2022] [Indexed: 12/14/2022]
Abstract
Inhaled nitric oxide (iNO) is a potent vasodilator approved for use in term and near-term neonates, but with broad off-label use in settings including acute respiratory distress syndrome (ARDS). As an inhaled therapy, iNO reaches well ventilated portions of the lung and selectively vasodilates the pulmonary vascular bed, with little systemic effect due to its rapid inactivation in the bloodstream. iNO is well documented to improve oxygenation in a variety of pathological conditions, but in ARDS, these transient improvements in oxygenation have not translated into meaningful clinical outcomes. In coronavirus disease 2019 (COVID-19) related ARDS, iNO has been proposed as a potential treatment due to a variety of mechanisms, including its vasodilatory effect, antiviral properties, as well as anti-thrombotic and anti-inflammatory actions. Presently however, no randomized controlled data are available evaluating iNO in COVID-19, and published data are largely derived from retrospective and cohort studies. It is therefore important to interpret these limited findings with caution, as many questions remain around factors such as patient selection, optimal dosing, timing of administration, duration of administration, and delivery method. Each of these factors may influence whether iNO is indeed an efficacious therapy - or not - in this context. As such, until randomized controlled trial data are available, use of iNO in the treatment of patients with COVID-19 related ARDS should be considered on an individual basis with sound clinical judgement from the attending physician.
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