651
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Contemporary strategies to improve clinical trial design for critical care research: insights from the First Critical Care Clinical Trialists Workshop. Intensive Care Med 2020; 46:930-942. [PMID: 32072303 PMCID: PMC7224097 DOI: 10.1007/s00134-020-05934-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 01/11/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Conducting research in critically-ill patient populations is challenging, and most randomized trials of critically-ill patients have not achieved pre-specified statistical thresholds to conclude that the intervention being investigated was beneficial. METHODS In 2019, a diverse group of patient representatives, regulators from the USA and European Union, federal grant managers, industry representatives, clinical trialists, epidemiologists, and clinicians convened the First Critical Care Clinical Trialists (3CT) Workshop to discuss challenges and opportunities in conducting and assessing critical care trials. Herein, we present the advantages and disadvantages of available methodologies for clinical trial design, conduct, and analysis, and a series of recommendations to potentially improve future trials in critical care. CONCLUSION The 3CT Workshop participants identified opportunities to improve critical care trials using strategies to optimize sample size calculations, account for patient and disease heterogeneity, increase the efficiency of trial conduct, maximize the use of trial data, and to refine and standardize the collection of patient-centered and patient-informed outcome measures beyond mortality.
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652
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Reyes M, Filbin MR, Bhattacharyya RP, Billman K, Eisenhaure T, Hung DT, Levy BD, Baron RM, Blainey PC, Goldberg MB, Hacohen N. An immune-cell signature of bacterial sepsis. Nat Med 2020; 26:333-340. [PMID: 32066974 DOI: 10.1038/s41591-020-0752-4] [Citation(s) in RCA: 229] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 01/03/2020] [Indexed: 12/17/2022]
Abstract
Dysregulation of the immune response to bacterial infection can lead to sepsis, a condition with high mortality. Multiple whole-blood gene-expression studies have defined sepsis-associated molecular signatures, but have not resolved changes in transcriptional states of specific cell types. Here, we used single-cell RNA-sequencing to profile the blood of people with sepsis (n = 29) across three clinical cohorts with corresponding controls (n = 36). We profiled total peripheral blood mononuclear cells (PBMCs, 106,545 cells) and dendritic cells (19,806 cells) across all subjects and, on the basis of clustering of their gene-expression profiles, defined 16 immune-cell states. We identified a unique CD14+ monocyte state that is expanded in people with sepsis and validated its power in distinguishing these individuals from controls using public transcriptomic data from subjects with different disease etiologies and from multiple geographic locations (18 cohorts, n = 1,467 subjects). We identified a panel of surface markers for isolation and quantification of the monocyte state and characterized its epigenomic and functional phenotypes, and propose a model for its induction from human bone marrow. This study demonstrates the utility of single-cell genomics in discovering disease-associated cytologic signatures and provides insight into the cellular basis of immune dysregulation in bacterial sepsis.
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Affiliation(s)
- Miguel Reyes
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael R Filbin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Roby P Bhattacharyya
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Center for Bacterial Pathogenesis, Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Deborah T Hung
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Bruce D Levy
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rebecca M Baron
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Paul C Blainey
- Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Marcia B Goldberg
- Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Center for Bacterial Pathogenesis, Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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653
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Inata Y. Should we treat sepsis-induced DIC with anticoagulants? J Intensive Care 2020; 8:18. [PMID: 32082582 PMCID: PMC7020366 DOI: 10.1186/s40560-020-0435-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Accepted: 01/23/2020] [Indexed: 12/11/2022] Open
Abstract
Background Disseminated intravascular coagulation (DIC) is a common complication in sepsis because of crosstalk between the immune system and the coagulation system. Several anticoagulant agents have been tested in an attempt to improve the survival of patients with sepsis and sepsis-induced DIC. Here, we discuss the rationale against using anticoagulation therapy in septic DIC. Main body of the abstract Coagulopathy and DIC are associated with increased mortality in sepsis. Several anticoagulant agents have been tested in an attempt to improve the survival of patients with sepsis and sepsis-induced DIC, but have proven largely ineffective. This is because of two major factors. First, the coagulation system is complex and closely related to the immune system. When we manipulate one of the factors involved in these systems, we may disturb the delicate homeostasis between them. A second factor may be failure to identify patients who will benefit from anticoagulation therapy. This may be attributed partly to the fact that there is no gold standard for the diagnosis of DIC, and there are consequently several diagnostic criteria, none of which are specifically designed for sepsis-induced DIC. Application of precision medicine, of the kind currently being applied in other intensive care fields, may be the key to overcoming these challenges. Until we know the precise target population, we should not use anticoagulation therapy in sepsis-induced DIC outside a research setting. Short conclusion There is no strong evidence to support the effectiveness of routine anticoagulation therapy in sepsis-induced DIC, and it should not be used clinically until more is known regarding the population of patients who may benefit from it.
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Affiliation(s)
- Yu Inata
- Department of Intensive Care Medicine, Osaka Women's and Children's Hospital, 840 Murodo-cho, Izumi, Osaka 594-1101 Japan
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654
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Luz CF, Vollmer M, Decruyenaere J, Nijsten MW, Glasner C, Sinha B. Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies. Clin Microbiol Infect 2020; 26:1291-1299. [PMID: 32061798 DOI: 10.1016/j.cmi.2020.02.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/01/2020] [Accepted: 02/03/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. OBJECTIVES To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. SOURCES A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. CONTENT Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. IMPLICATIONS Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.
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Affiliation(s)
- C F Luz
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands.
| | - M Vollmer
- Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany
| | - J Decruyenaere
- Ghent University, Ghent University Hospital, Department of Intensive Care, Ghent, Belgium
| | - M W Nijsten
- University of Groningen, University Medical Center Groningen, Department of Critical Care, Groningen, the Netherlands
| | - C Glasner
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands
| | - B Sinha
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands
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655
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Molano Franco D, Gómez Duque M, Beltrán E, Villabón González M, Robayo Valbuena IF, Franco LF, Cárdenas Colmenares JA, Estupiñán Monsalve Á, Sánchez Vanegas G, Arévalo Rodriguez I, Zamora Romero J. Medicina de precisión en sepsis: utilidad de los biomarcadores en pacientes biomarcadores en pacientes críticamente enfermos. REPERTORIO DE MEDICINA Y CIRUGÍA 2020. [DOI: 10.31260/repertmedcir.01217273.973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Durante años la evolución del cuidado intensivo ha intentado ofrecer una atención basada en protocolos y paquetes de manejo agrupados por patologías y cuadro sindromáticos. Aunque se logró disminuir la mortalidad en diferentes patologías (sepsis y síndromes coronario agudo y de distrés respiratorio agudo), no se han resuelto por completo los problemas clínicos, en especial el diagnóstico y el manejo. Una nueva opción ha surgido en el horizonte denominada “medicina de precisión”, entendida como estrategia de prevención y tratamiento que tiene en cuenta la variabilidad individual. La sepsis es un síndrome con múltiples aristas en cuanto al fenotipo y genotipo, cuyo diagnóstico temprano es relevante para los desenlaces clínicos. Hasta el momento el enfoque principal ha sido la identificación de un germen etiológico para diferenciarla del síndrome de respuesta inflamatoria sistémica (SIRS). En los últimos años el paradigma en enfermedades infecciosas ha cambiado debido a estudios que demuestran como la respuesta inmunitaria del paciente séptico tiene un papel clave en el desarrollo de la enfermedad, con implicaciones en el diagnóstico, pronóstico y tratamiento, que podrían ayudar a cambiar el abordaje en los próximos años gracias a una estrategia basada en medicina de precisión. Hoy los aislamientos microbiológicos y los cultivos siguen siendo el estándar de referencia con varias desventajas como el tiempo para obtener resultados, sobre todo en infecciones por gérmenes resistentes u hongos, que pueden retrasar el inicio de la terapia antimicrobiana. Como alternativa se ha planteado el uso de biomarcadores en sepsis que siendo productos de la respuesta inflamatoria del individuo ante la infección, son útiles para el diagnóstico y pronóstico primordialmente en los críticamente enfermos. Decidimos realizar esta revisión narrativa acerca de la utilidad de los biomarcadores en pacientes con sepsis críticamente enfermos, para enfocarlos en un modelo de medicina personalizada.
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656
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657
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Stroup EK, Luo Y, Sanchez-Pinto LN. Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2020; 2019:968-972. [PMID: 33842023 DOI: 10.1109/bibm47256.2019.8983126] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two children's hospitals and used subgraph-augmented non-negative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.
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Affiliation(s)
- Emily Kunce Stroup
- Driskill Graduate Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, U.S.A
| | - Yuan Luo
- Dept. of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, U.S.A
| | - L Nelson Sanchez-Pinto
- Depts. of Pediatrics and Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, U.S.A
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658
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Mlodzinski E, Stone DJ, Celi LA. Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review. Pulm Ther 2020; 6:67-77. [PMID: 32048244 PMCID: PMC7229087 DOI: 10.1007/s41030-020-00110-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Indexed: 01/22/2023] Open
Abstract
Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.
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Affiliation(s)
- Eric Mlodzinski
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
| | - David J Stone
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Departments of Anesthesiology and Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.,Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Leo A Celi
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA
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659
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Prasad PA, Fang MC, Abe-Jones Y, Calfee CS, Matthay MA, Kangelaris KN. Time to Recognition of Sepsis in the Emergency Department Using Electronic Health Record Data: A Comparative Analysis of Systemic Inflammatory Response Syndrome, Sequential Organ Failure Assessment, and Quick Sequential Organ Failure Assessment. Crit Care Med 2020; 48:200-209. [PMID: 31939788 PMCID: PMC7494056 DOI: 10.1097/ccm.0000000000004132] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Early identification of sepsis is critical to improving patient outcomes. Impact of the new sepsis definition (Sepsis-3) on timing of recognition in the emergency department has not been evaluated. Our study objective was to compare time to meeting systemic inflammatory response syndrome (Sepsis-2) criteria, Sequential Organ Failure Assessment (Sepsis-3) criteria, and quick Sequential Organ Failure Assessment criteria using electronic health record data. DESIGN Retrospective, observational study. SETTING The emergency department at the University of California, San Francisco. PATIENTS Emergency department encounters between June 2012 and December 2016 for patients greater than or equal to 18 years old with blood cultures ordered, IV antibiotic receipt, and identification with sepsis via systemic inflammatory response syndrome or Sequential Organ Failure Assessment within 72 hours of emergency department presentation. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We analyzed timestamped electronic health record data from 16,612 encounters identified as sepsis by greater than or equal to 2 systemic inflammatory response syndrome criteria or a Sequential Organ Failure Assessment score greater than or equal to 2. The primary outcome was time from emergency department presentation to meeting greater than or equal to 2 systemic inflammatory response syndrome criteria, Sequential Organ Failure Assessment greater than or equal to 2, and/or greater than or equal to 2 quick Sequential Organ Failure Assessment criteria. There were 9,087 patients (54.7%) that met systemic inflammatory response syndrome-first a median of 26 minutes post-emergency department presentation (interquartile range, 0-109 min), with 83.1% meeting Sequential Organ Failure Assessment criteria a median of 118 minutes later (interquartile range, 44-401 min). There were 7,037 patients (42.3%) that met Sequential Organ Failure Assessment-first, a median of 113 minutes post-emergency department presentation (interquartile range, 60-251 min). Quick Sequential Organ Failure Assessment was met in 46.4% of patients a median of 351 minutes post-emergency department presentation (interquartile range, 67-1,165 min). Adjusted odds of in-hospital mortality were 39% greater in patients who met systemic inflammatory response syndrome-first compared with those who met Sequential Organ Failure Assessment-first (odds ratio, 1.39; 95% CI, 1.20-1.61). CONCLUSIONS Systemic inflammatory response syndrome and Sequential Organ Failure Assessment initially identified distinct populations. Using systemic inflammatory response syndrome resulted in earlier electronic health record sepsis identification in greater than 50% of patients. Using Sequential Organ Failure Assessment alone may delay identification. Using systemic inflammatory response syndrome alone may lead to missed sepsis presenting as acute organ dysfunction. Thus, a combination of inflammatory (systemic inflammatory response syndrome) and organ dysfunction (Sequential Organ Failure Assessment) criteria may enhance timely electronic health record-based sepsis identification.
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Affiliation(s)
- Priya A. Prasad
- Division of Hospital Medicine, University of California, San Francisco
| | - Margaret C. Fang
- Division of Hospital Medicine, University of California, San Francisco
| | - Yumiko Abe-Jones
- Division of Hospital Medicine, University of California, San Francisco
| | - Carolyn S. Calfee
- Pulmonary and Critical Care Medicine, University of California, San Francisco
| | - Michael A. Matthay
- Cardiovascular Research Institute, University of California San Francisco
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660
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Abstract
Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality despite adequate antibiotic therapy. It is the single most common cause of infection-related mortality in the United States. An exaggerated host inflammatory response can potentially be harmful to both the lung and host, and has been associated with treatment failure and mortality. Modulation of inflammatory response may, therefore, be theoretically beneficial. The anti-inflammatory and immunosuppressive effects of steroids seem an attractive therapeutic option in severe CAP patients. Available datapoint to overall shorter time to clinical stability and decreased length-of-stay in CAP patients, with a potential mortality benefit in severe CAP. The level of evidence is, however, low to moderate regarding mortality due to high heterogeneity and insufficient power of data. Furthermore, steroids were deleterious in influenza pneumonia and in patients with pneumococcal pneumonia data suggest a lack of efficacy and potential harm. Both European and American guidelines recommend not using corticosteroids in CAP. Patients who might benefit and those that can be harmed from steroids remain to be clearly identified, as does the ideal steroid for CAP patients, based on pharmacokinetic and pharmacodynamic properties. It is essential for future studies to avoid the same methodological bias present in the available data so that high-quality evidence on the true role of steroids in CAP can be provided.
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Affiliation(s)
- David Nora
- Polyvalent Intensive Care Unit, São Francisco Xavier Hospital, Centro Hospitalar De Lisboa Ocidental, Lisbon, Portugal.,NOVA Medical School, CHRC, New University of Lisbon, Lisbon, Portugal
| | - Wagner Nedel
- Intensive Care Unit, Hospital Nossa Senhora Da Conceição, Porto Alegre, Brazil
| | - Thiago Lisboa
- Critical Care Department, Hospital De Clínicas De Porto Alegre, Post-Graduation Program (PPG) Pneumology,Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil
| | - Jorge Salluh
- D'or Institute for Research and Education, Rio De Janeiro, Brazil
| | - Pedro Póvoa
- Polyvalent Intensive Care Unit, São Francisco Xavier Hospital, Centro Hospitalar De Lisboa Ocidental, Lisbon, Portugal.,NOVA Medical School, CHRC, New University of Lisbon, Lisbon, Portugal.,Center for Clinical Epidemiology and Research Unit of Clinical Epidemiology, OUH Odense University Hospital, Denmark
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661
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Textoris J. Immunity check should be performed for all patients with septic shock? Yes. Intensive Care Med 2020; 46:503-505. [PMID: 31965264 PMCID: PMC7223434 DOI: 10.1007/s00134-019-05909-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 12/19/2019] [Indexed: 12/29/2022]
Affiliation(s)
- Julien Textoris
- EA7426 "Pathophysiology of Injury-Induced Immunosuppression", PI3, Université Claude Bernard Lyon-1, Hospices Civils de Lyon, bioMérieux, Lyon, France.
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662
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Tao L, Zhang C, Zeng L, Zhu S, Li N, Li W, Zhang H, Zhao Y, Zhan S, Ji H. Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice-Aided Diagnosis: Interrupted Time Series Study. JMIR Med Inform 2020; 8:e16912. [PMID: 31958069 PMCID: PMC6997922 DOI: 10.2196/16912] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 12/02/2019] [Accepted: 12/15/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. OBJECTIVE The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice-aided diagnosis in real-world research. METHODS This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. RESULTS The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all P<.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, P=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, P=.01). Similar results were obtained in the subgroup analysis. CONCLUSIONS The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians' diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.
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Affiliation(s)
- Liyuan Tao
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Chen Zhang
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Lin Zeng
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Shengrong Zhu
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Nan Li
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Wei Li
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Hua Zhang
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Yiming Zhao
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Siyan Zhan
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Hong Ji
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
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663
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Wiedermann CJ. Anticoagulant therapy for septic coagulopathy and disseminated intravascular coagulation: where do KyberSept and SCARLET leave us? Acute Med Surg 2020; 7:e477. [PMID: 31988789 PMCID: PMC6971424 DOI: 10.1002/ams2.477] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 11/18/2019] [Indexed: 12/12/2022] Open
Abstract
The use of antithrombin and thrombomodulin to restore impaired anticoagulant pathways in septic coagulopathy has been shown to significantly increase the resolution rate of disseminated intravascular coagulation. In KyberSept and SCARLET, two large, international, randomized controlled trials in patients with sepsis, these anticoagulants have not shown significantly reduced mortality. The aim of this assessment was to compare the heterogeneity in responses to treatment in the two trials according to different patient phenotypes. Results of the KyberSept and SCARLET trials reported in original and post-hoc publications were analyzed and directly compared for treatment effects in various patient subgroups. In both KyberSept and SCARLET, the septic coagulopathy phenotype that benefited most from endogenous anticoagulant supplementation showed markers of excessive activation of coagulation. Interaction between concomitant thromboprophylactic heparin and the endogenous anticoagulants abrogated the efficacy of both antithrombin and thrombomodulin. In both trials, higher disease severity was associated with better treatment outcome. In conclusion, in two landmark studies of endogenous anticoagulants in patients with sepsis, similar findings of beneficial effects in the coagulopathy phenotype and interactions with heparin comedication and disease severity support the potential roles that thrombomodulin and antithrombin might play in treating septic coagulopathy and disseminated intravascular coagulation. Further prospective validation is warranted. Future trial designs to definitively establish the therapeutic relevance of antithrombin and thrombomodulin in septic coagulopathy should focus on involvement of patients characterized by coagulopathy and disease severity as well as interactions between endogenous anticoagulants and exogenous heparin.
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Affiliation(s)
- Christian J Wiedermann
- Institute of Public Health Medical Decision Making and HTA Medical Informatics and Technology University of Health Sciences Hall Austria
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664
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The damage response framework and infection prevention: From concept to bedside. Infect Control Hosp Epidemiol 2020; 41:337-341. [PMID: 31915082 DOI: 10.1017/ice.2019.354] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Hospital-acquired infections remain a common cause of morbidity and mortality despite advances in infection prevention through use of bundles, environmental cleaning, antimicrobial stewardship, and other best practices. Current prevention strategies and further hospital-acquired infection reduction are limited by lack of recognition of the role that host-microbe interactions play in susceptibility and by the inability to analyze multiple risk factors in real time to accurately predict the likelihood of a hospital-acquired infection before it occurs and to inform medical decision making. Herein, we examine the value of incorporating the damage-response framework and host attributes that determine susceptibility to infectious diseases known by the acronym MISTEACHING (ie, microbiome, immunity, sex, temperature, environment, age, chance, history, inoculum, nutrition, genetics) into infection prevention strategies using machine learning to drive decision support and patient-specific interventions.
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665
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GDF3 Protects Mice against Sepsis-Induced Cardiac Dysfunction and Mortality by Suppression of Macrophage Pro-Inflammatory Phenotype. Cells 2020; 9:cells9010120. [PMID: 31947892 PMCID: PMC7017037 DOI: 10.3390/cells9010120] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 12/31/2019] [Accepted: 01/01/2020] [Indexed: 12/11/2022] Open
Abstract
Macrophages are critical for regulation of inflammatory response during endotoxemia and septic shock. However, the mediators underlying their regulatory function remain obscure. Growth differentiation factor 3 (GDF3), a member of transforming growth factor beta (TGF-β) superfamily, has been implicated in inflammatory response. Nonetheless, the role of GDF3 in macrophage-regulated endotoxemia/sepsis is unknown. Here, we show that serum GDF3 levels in septic patients are elevated and strongly correlate with severity of sepsis and 28-day mortality. Interestingly, macrophages treated with recombinant GDF3 protein (rGDF3) exhibit greatly reduced production of pro-inflammatory cytokines, comparing to controls upon endotoxin challenge. Moreover, acute administration of rGDF3 to endotoxin-treated mice suppresses macrophage infiltration to the heart, attenuates systemic and cardiac inflammation with less pro-inflammatory macrophages (M1) and more anti-inflammatory macrophages (M2), as well as prolongs mouse survival. Mechanistically, GDF3 is able to activate Smad2/Smad3 phosphorylation, and consequently inhibits the expression of nod-like receptor protein-3 (NLRP3) in macrophages. Accordingly, blockade of Smad2/Smad3 phosphorylation with SB431542 significantly offsets rGDF3-mediated anti-inflammatory effects. Taken together, this study uncovers that GDF3, as a novel sepsis-associated factor, may have a dual role in the pathophysiology of sepsis. Acute administration of rGDF3 into endotoxic shock mice could increase survival outcome and improve cardiac function through anti-inflammatory response by suppression of M1 macrophage phenotype. However, constitutive high levels of GDF3 in human sepsis patients are associated with lethality, suggesting that GDF3 may promote macrophage polarization toward M2 phenotype which could lead to immunosuppression.
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666
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Caraballo C, Jaimes F. Organ Dysfunction in Sepsis: An Ominous Trajectory From Infection To Death. THE YALE JOURNAL OF BIOLOGY AND MEDICINE 2019; 92:629-640. [PMID: 31866778 PMCID: PMC6913810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Sepsis is a highly complex and lethal syndrome with highly heterogeneous clinical manifestations that makes it difficult to detect and treat. It is also one of the major and most urgent global public health challenges. More than 30 million people are diagnosed with sepsis each year, with 5 million attributable deaths and long-term sequalae among survivors. The current international consensus defines sepsis as a life-threatening organ dysfunction caused by a dysregulated host response to an infection. Over the past decades substantial research has increased the understanding of its pathophysiology. The immune response induces a severe macro and microcirculatory dysfunction that leads to a profound global hypoperfusion, injuring multiple organs. Consequently, patients with sepsis might present dysfunction of virtually any system, regardless of the site of infection. The organs more frequently affected are kidneys, liver, lungs, heart, central nervous system, and hematologic system. This multiple organ failure is the hallmark of sepsis and determines patients' course from infection to recovery or death. There are tools to assess the severity of the disease that can also help to guide treatment, like the Sequential Organ Failure Assessment (SOFA) score. However, sepsis disease process is vastly heterogeneous, which could explain why interventions targeted to directly intervene its mechanisms have shown unsuccessful results and predicting outcomes with accuracy is still elusive. Thus, it is required to implement strong public health strategies and leverage novel technologies in research to improve outcomes and mitigate the burden of sepsis and septic shock worldwide.
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Affiliation(s)
- César Caraballo
- Center for Outcomes Research and Evaluation, Yale New Haven Health, New Haven, CT, USA
| | - Fabián Jaimes
- Academic Group of Clinical Epidemiology, School of Medicine, University of Antioquia, Medellín, Colombia,Department of Internal Medicine, School of Medicine, University of Antioquia, Medellín, Colombia,Research Direction, San Vicente Foundation University Hospital, Medellín, Colombia,To whom all correspondence should be addressed: Dr. Fabián Jaimes, Hospital San Vicente Fundación, Calle 64 # 51 D-154, Medellín, Antioquia, Colombia; Tel: +57 (4) 2192433,
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667
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Ohukainen P, Kuusisto S, Kettunen J, Perola M, Järvelin MR, Mäkinen VP, Ala-Korpela M. Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease. Atherosclerosis 2019; 294:10-15. [PMID: 31931463 DOI: 10.1016/j.atherosclerosis.2019.12.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/02/2019] [Accepted: 12/12/2019] [Indexed: 01/14/2023]
Abstract
BACKGROUND AND AIMS Population subgrouping has been suggested as means to improve coronary heart disease (CHD) risk assessment. We explored here how unsupervised data-driven metabolic subgrouping, based on comprehensive lipoprotein subclass data, would work in large-scale population cohorts. METHODS We applied a self-organizing map (SOM) artificial intelligence methodology to define subgroups based on detailed lipoprotein profiles in a population-based cohort (n = 5789) and utilised the trained SOM in an independent cohort (n = 7607). We identified four SOM-based subgroups of individuals with distinct lipoprotein profiles and CHD risk and compared those to univariate subgrouping by apolipoprotein B quartiles. RESULTS The SOM-based subgroup with highest concentrations for non-HDL measures had the highest, and the subgroup with lowest concentrations, the lowest risk for CHD. However, apolipoprotein B quartiles produced better resolution of risk than the SOM-based subgroups and also striking dose-response behaviour. CONCLUSIONS These results suggest that the majority of lipoprotein-mediated CHD risk is explained by apolipoprotein B-containing lipoprotein particles. Therefore, even advanced multivariate subgrouping, with comprehensive data on lipoprotein metabolism, may not advance CHD risk assessment.
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Affiliation(s)
- Pauli Ohukainen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Sanna Kuusisto
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Johannes Kettunen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; National Institute for Health and Welfare, Helsinki, Finland
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland; Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland; Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; Department of Life Sciences, College of Health and Life Sciences, Brunel University London, UK
| | - Ville-Petteri Mäkinen
- Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Australia; Hopwood Centre for Neurobiology, Lifelong Health Theme, SAHMRI, Australia
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland; Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
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668
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Clinical management of sepsis can be improved by artificial intelligence: yes. Intensive Care Med 2019; 46:375-377. [PMID: 31834423 DOI: 10.1007/s00134-019-05898-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 12/09/2019] [Indexed: 12/21/2022]
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Ethical considerations about artificial intelligence for prognostication in intensive care. Intensive Care Med Exp 2019; 7:70. [PMID: 31823128 PMCID: PMC6904702 DOI: 10.1186/s40635-019-0286-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 11/28/2019] [Indexed: 11/25/2022] Open
Abstract
Background Prognosticating the course of diseases to inform decision-making is a key component of intensive care medicine. For several applications in medicine, new methods from the field of artificial intelligence (AI) and machine learning have already outperformed conventional prediction models. Due to their technical characteristics, these methods will present new ethical challenges to the intensivist. Results In addition to the standards of data stewardship in medicine, the selection of datasets and algorithms to create AI prognostication models must involve extensive scrutiny to avoid biases and, consequently, injustice against individuals or groups of patients. Assessment of these models for compliance with the ethical principles of beneficence and non-maleficence should also include quantification of predictive uncertainty. Respect for patients’ autonomy during decision-making requires transparency of the data processing by AI models to explain the predictions derived from these models. Moreover, a system of continuous oversight can help to maintain public trust in this technology. Based on these considerations as well as recent guidelines, we propose a pathway to an ethical implementation of AI-based prognostication. It includes a checklist for new AI models that deals with medical and technical topics as well as patient- and system-centered issues. Conclusion AI models for prognostication will become valuable tools in intensive care. However, they require technical refinement and a careful implementation according to the standards of medical ethics.
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670
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Kitsios GD, Yang L, Manatakis DV, Nouraie M, Evankovich J, Bain W, Dunlap DD, Shah F, Barbash IJ, Rapport SF, Zhang Y, DeSensi RS, Weathington NM, Chen BB, Ray P, Mallampalli RK, Benos PV, Lee JS, Morris A, McVerry BJ. Host-Response Subphenotypes Offer Prognostic Enrichment in Patients With or at Risk for Acute Respiratory Distress Syndrome. Crit Care Med 2019; 47:1724-1734. [PMID: 31634231 PMCID: PMC6865808 DOI: 10.1097/ccm.0000000000004018] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
OBJECTIVES Classification of patients with acute respiratory distress syndrome into hyper- and hypoinflammatory subphenotypes using plasma biomarkers may facilitate more effective targeted therapy. We examined whether established subphenotypes are present not only in patients with acute respiratory distress syndrome but also in patients at risk for acute respiratory distress syndrome (ARFA) and then assessed the prognostic information of baseline subphenotyping on the evolution of host-response biomarkers and clinical outcomes. DESIGN Prospective, observational cohort study. SETTING Medical ICU at a tertiary academic medical center. PATIENTS Mechanically ventilated patients with acute respiratory distress syndrome or ARFA. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We performed longitudinal measurements of 10 plasma biomarkers of host injury and inflammation. We applied unsupervised latent class analysis methods utilizing baseline clinical and biomarker variables and demonstrated that two-class models (hyper- vs hypoinflammatory subphenotypes) offered improved fit compared with one-class models in both patients with acute respiratory distress syndrome and ARFA. Baseline assignment to the hyperinflammatory subphenotype (39/104 [38%] acute respiratory distress syndrome and 30/108 [28%] ARFA patients) was associated with higher severity of illness by Sequential Organ Failure Assessment scores and incidence of acute kidney injury in patients with acute respiratory distress syndrome, as well as higher 30-day mortality and longer duration of mechanical ventilation in ARFA patients (p < 0.0001). Hyperinflammatory patients exhibited persistent elevation of biomarkers of innate immunity for up to 2 weeks postintubation. CONCLUSIONS Our results suggest that two distinct subphenotypes are present not only in patients with established acute respiratory distress syndrome but also in patients at risk for its development. Hyperinflammatory classification at baseline is associated with higher severity of illness, worse clinical outcomes, and trajectories of persistently elevated biomarkers of host injury and inflammation during acute critical illness compared with hypoinflammatory patients. Our findings provide strong rationale for examining treatment effect modifications by subphenotypes in randomized clinical trials to inform precision therapeutic approaches in critical care.
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Affiliation(s)
- Georgios D. Kitsios
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh
| | - Libing Yang
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Dimitris V. Manatakis
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mehdi Nouraie
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - John Evankovich
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - William Bain
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Daniel D. Dunlap
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Faraaz Shah
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ian J Barbash
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Sarah F. Rapport
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Yingze Zhang
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Rebecca S. DeSensi
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Nathaniel M. Weathington
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Bill B. Chen
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Prabir Ray
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Rama K. Mallampalli
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System
| | - Panayiotis V. Benos
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Janet S. Lee
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Alison Morris
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Bryan J. McVerry
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Center for Medicine and the Microbiome, University of Pittsburgh
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671
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Weiss SL, Peters MJ. Risks and benefits of fluid bolus therapy: the need for a good explanation. Arch Dis Child 2019; 104:1125-1126. [PMID: 31444212 DOI: 10.1136/archdischild-2019-317789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 08/02/2019] [Accepted: 08/07/2019] [Indexed: 11/04/2022]
Affiliation(s)
- Scott L Weiss
- Department of Anesthesiology and Critical Care, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Mark John Peters
- Paediatric Intensive Care, Great Ormond St Hospital NHS Trust, London, UK
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672
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Seymour CW, Kerti SJ, Lewis AJ, Kennedy J, Brant E, Griepentrog JE, Zhang X, Angus DC, Chang CCH, Rosengart MR. Murine sepsis phenotypes and differential treatment effects in a randomized trial of prompt antibiotics and fluids. Crit Care 2019; 23:384. [PMID: 31779663 PMCID: PMC6883631 DOI: 10.1186/s13054-019-2655-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 10/21/2019] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Clinical and biologic phenotypes of sepsis are proposed in human studies, yet it is unknown whether prognostic or drug response phenotypes are present in animal models of sepsis. Using a biotelemetry-enhanced, murine cecal ligation and puncture (CLP) model, we determined phenotypes of polymicrobial sepsis prior to physiologic deterioration, and the association between phenotypes and outcome in a randomized trial of prompt or delayed antibiotics and fluids. METHODS We performed a secondary analysis of male C57BL/6J mice in two observational cohorts and two randomized, laboratory animal experimental trials. In cohort 1, mice (n = 118) underwent biotelemetry-enhanced CLP, and we applied latent class mixed models to determine optimal number of phenotypes using clinical data collected between injury and physiologic deterioration. In cohort 2 (N = 73 mice), inflammatory cytokines measured at 24 h after deterioration were explored by phenotype. In a subset of 46 mice enrolled in two trials from cohort 1, we tested the association of phenotypes with the response to immediate (0 h) vs. delayed (2 to 4 h) antibiotics or fluids initiated after physiologic deterioration. RESULTS Latent class mixture modeling derived a two-class model in cohort 1. Class 2 (N = 97) demonstrated a shorter time to deterioration (mean SD 7.3 (0.9) vs. 9.7 (3.2) h, p < 0.001) and lower heart rate at 7 h after injury (mean (SD) 564 (55) vs. 626 (35) beats per minute, p < 0.001). Overall mortality was similar between phenotypes (p = 0.75). In cohort 2 used for biomarker measurement, class 2 mice had greater plasma concentrations of IL6 and IL10 at 24 h after CLP (p = 0.05). In pilot randomized trials, the effects of sepsis treatment (immediate vs. delayed antibiotics) differed by phenotype (p = 0.03), with immediate treatment associated with greater survival in class 2 mice only. Similar differential treatment effect by class was observed in the trial of immediate vs. delayed fluids (p = 0.02). CONCLUSIONS We identified two sepsis phenotypes in a murine cecal ligation and puncture model, one of which is characterized by faster deterioration and more severe inflammation. Response to treatment in a randomized trial of immediate versus delayed antibiotics and fluids differed on the basis of phenotype.
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Affiliation(s)
- Christopher W. Seymour
- 0000 0004 1936 9000grid.21925.3dDepartments of Critical Care Medicine Emergency Medicine, University of Pittsburgh School of Medicine, 3550 Terrace St, Scaife Hall, #639, Pittsburgh, PA 15261 USA ,0000 0004 1936 9000grid.21925.3dClinical Research, Investigation, and Systems Modeling of Acute Illness Center (CRISMA), University of Pittsburgh School of Medicine, Pittsburgh, USA ,0000 0004 1936 9000grid.21925.3dDepartment of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Samantha J. Kerti
- 0000 0004 1936 9000grid.21925.3dDepartments of Critical Care Medicine Emergency Medicine, University of Pittsburgh School of Medicine, 3550 Terrace St, Scaife Hall, #639, Pittsburgh, PA 15261 USA ,0000 0004 1936 9000grid.21925.3dClinical Research, Investigation, and Systems Modeling of Acute Illness Center (CRISMA), University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Anthony J. Lewis
- 0000 0004 1936 9000grid.21925.3dDepartment of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Jason Kennedy
- 0000 0004 1936 9000grid.21925.3dDepartments of Critical Care Medicine Emergency Medicine, University of Pittsburgh School of Medicine, 3550 Terrace St, Scaife Hall, #639, Pittsburgh, PA 15261 USA ,0000 0004 1936 9000grid.21925.3dClinical Research, Investigation, and Systems Modeling of Acute Illness Center (CRISMA), University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Emily Brant
- 0000 0004 1936 9000grid.21925.3dDepartments of Critical Care Medicine Emergency Medicine, University of Pittsburgh School of Medicine, 3550 Terrace St, Scaife Hall, #639, Pittsburgh, PA 15261 USA ,0000 0004 1936 9000grid.21925.3dClinical Research, Investigation, and Systems Modeling of Acute Illness Center (CRISMA), University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - John E. Griepentrog
- 0000 0004 1936 9000grid.21925.3dDepartment of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Xianghong Zhang
- 0000 0004 1936 9000grid.21925.3dDepartment of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Derek C. Angus
- 0000 0004 1936 9000grid.21925.3dDepartments of Critical Care Medicine Emergency Medicine, University of Pittsburgh School of Medicine, 3550 Terrace St, Scaife Hall, #639, Pittsburgh, PA 15261 USA ,0000 0004 1936 9000grid.21925.3dClinical Research, Investigation, and Systems Modeling of Acute Illness Center (CRISMA), University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Chung-Chou H. Chang
- 0000 0004 1936 9000grid.21925.3dDepartments of Critical Care Medicine Emergency Medicine, University of Pittsburgh School of Medicine, 3550 Terrace St, Scaife Hall, #639, Pittsburgh, PA 15261 USA ,0000 0004 1936 9000grid.21925.3dClinical Research, Investigation, and Systems Modeling of Acute Illness Center (CRISMA), University of Pittsburgh School of Medicine, Pittsburgh, USA ,0000 0004 1936 9000grid.21925.3dDepartment of Biostatistics, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Matthew R. Rosengart
- 0000 0004 1936 9000grid.21925.3dDepartments of Critical Care Medicine Emergency Medicine, University of Pittsburgh School of Medicine, 3550 Terrace St, Scaife Hall, #639, Pittsburgh, PA 15261 USA ,0000 0004 1936 9000grid.21925.3dClinical Research, Investigation, and Systems Modeling of Acute Illness Center (CRISMA), University of Pittsburgh School of Medicine, Pittsburgh, USA ,0000 0004 1936 9000grid.21925.3dDepartment of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, USA
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Greco M, Mazzei A, Palumbo C, Verri T, Lobreglio G. Flow Cytometric Analysis of Monocytes Polarization and Reprogramming From Inflammatory to Immunosuppressive Phase During Sepsis. EJIFCC 2019; 30:371-384. [PMID: 31814812 PMCID: PMC6893894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Sepsis outcome is determined by a balance between inflammation and immune suppression. We aimed to evaluate monocytes polarization and reprogramming during these processes. We analyzed 93 patients with procalcitonin level >0.5 ng/mL (hPCT) and suspected/confirmed sepsis, and 84 controls by analysis of CD14, CD16 and HLA-DR expression on blood monocytes using fluorescent labeled monoclonal antibodies and BD FACS CANTO II. Complete blood cell count, procalcitonin and other biochemical markers were evaluated. Intermediate monocytes CD14++CD16+ increased in hPCT patients (including both positive and negative culture) compared to controls (13.6% ± 0.8 vs 6.2% ± 0.3, p<0.001), while classical monocytes CD14++CD16-were significantly reduced (72.5% ± 1.6 vs 82.6% ± 0.7, p<0.001). Among hPCT patients having positive microbial culture, the percentage of intermediate monocytes was significantly higher in septic compared with non-septic/localized-infection patients (17.4% vs 11.5%; p<0.05) whilst the percentage of classical monocytes was lower (68.0% vs 74.5%). Three-four days following the diagnosis of sepsis, HLA-DR expression on monocyte (mHLA-DR) was lower (94.3%) compared to controls (99.4%) (p<0.05). Septic patients with the worst clinical conditions showed higher incidence of secondary infections, longtime hospitalization and lower HLA-DR+ monocytes compared to septic patients with better clinical outcome (88.4% vs 98.6%, p=0.05). The dynamic nature of sepsis correlates with monocytes functional polarization and reprogramming from a pro-inflammatory CD14++CD16+ phenotype in non-septic hPCT patients to a decrease of HLA-DR surface expression in hPCT patients with confirmed sepsis, making HLA-DR reduction a marker of immune-paralysis and sepsis outcome. Analysis of monocytes plasticity opens to new mechanisms responsible for pro/anti-inflammatory responses during sepsis, and new immunotherapies.
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Affiliation(s)
- Marilena Greco
- Clinical Pathology and Microbiology Laboratory, Vito Fazzi General Hospital ASL-Lecce, Lecce, Italy,Corresponding author: Marilena Greco, PhD Clinical Pathology and Microbiology Laboratory Vito Fazzi General Hospital ASL-Lecce Piazza Muratore 73100 Lecce Italy E-mail:
| | - Aurora Mazzei
- Laboratory of Physiology, Department of Biological and Environmental Sciences and Technologies (DeBEST), University of Salento, Lecce, Italy
| | - Claudio Palumbo
- Clinical Pathology and Microbiology Laboratory, Vito Fazzi General Hospital ASL-Lecce, Lecce, Italy
| | - Tiziano Verri
- Laboratory of Physiology, Department of Biological and Environmental Sciences and Technologies (DeBEST), University of Salento, Lecce, Italy
| | - Giambattista Lobreglio
- Clinical Pathology and Microbiology Laboratory, Vito Fazzi General Hospital ASL-Lecce, Lecce, Italy
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674
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Murao S, Yamakawa K. A Systematic Summary of Systematic Reviews on Anticoagulant Therapy in Sepsis. J Clin Med 2019; 8:E1869. [PMID: 31689983 PMCID: PMC6912821 DOI: 10.3390/jcm8111869] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 11/01/2019] [Indexed: 01/11/2023] Open
Abstract
Many systematic reviews have been published regarding anticoagulant therapy in sepsis, among which there is substantial heterogeneity. This study aimed to provide an overview of existing systematic reviews of randomized controlled trials by using a comprehensive search method. We searched MEDLINE, EMBASE, and Cochrane Database of Systematic Reviews. Of 895 records screened, 19 systematic reviews were included. The target agent was as follows: antithrombin (n = 4), recombinant thrombomodulin (n = 3), heparin (n = 3), recombinant activated protein C (n = 8), and all anticoagulants (n = 1). Antithrombin did not improve mortality in critically ill patients but indicated a beneficial effect in sepsis-induced disseminated intravascular coagulation (DIC), although the certainty of evidence was judged as low. Recombinant thrombomodulin was associated with a trend in reduced mortality in sepsis with coagulopathy with no increased risk of bleeding, although the difference was not statistically significant and the required information size for any declarative judgement insufficient. Although three systematic reviews showed potential survival benefits of unfractionated heparin and low-molecular-weight heparin in patients with sepsis, trials with low risk of bias were lacking, and the overall impact remains unclear. None of the meta-analyses of recombinant activated protein C showed beneficial effects in sepsis. In summary, a beneficial effect was not observed in overall sepsis in poorly characterized patient groups but was observed in sepsis-induced DIC or sepsis with coagulopathy in more specific patient groups. This umbrella review of anticoagulant therapy suggests that characteristics of the target populations resulted in heterogeneity among the systematic reviews.
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Affiliation(s)
- Shuhei Murao
- Division of Trauma and Surgical Critical Care, Osaka General Medical Center, Osaka 558-8558, Japan.
| | - Kazuma Yamakawa
- Division of Trauma and Surgical Critical Care, Osaka General Medical Center, Osaka 558-8558, Japan.
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675
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Davis FM, Schaller MA, denDekker A, Joshi AD, Kimball AS, Evanoff H, Wilke C, Obi AT, Melvin WJ, Cavassani K, Scola M, Carson B, Moser S, Blanc V, Engoren M, Moore BB, Kunkel SL, Gallagher KA. Sepsis Induces Prolonged Epigenetic Modifications in Bone Marrow and Peripheral Macrophages Impairing Inflammation and Wound Healing. Arterioscler Thromb Vasc Biol 2019; 39:2353-2366. [PMID: 31644352 PMCID: PMC6818743 DOI: 10.1161/atvbaha.119.312754] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/23/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Sepsis represents an acute life-threatening disorder resulting from a dysregulated host response. For patients who survive sepsis, there remains long-term consequences, including impaired inflammation, as a result of profound immunosuppression. The mechanisms involved in this long-lasting deficient immune response are poorly defined. Approach and Results: Sepsis was induced using the murine model of cecal ligation and puncture. Following a full recovery period from sepsis physiology, mice were subjected to our wound healing model and wound macrophages (CD11b+, CD3-, CD19-, Ly6G-) were sorted. Post-sepsis mice demonstrated impaired wound healing and decreased reepithelization in comparison to controls. Further, post-sepsis bone marrow-derived macrophages and wound macrophages exhibited decreased expression of inflammatory cytokines vital for wound repair (IL [interleukin]-1β, IL-12, and IL-23). To evaluate if decreased inflammatory gene expression was secondary to epigenetic modification, we conducted chromatin immunoprecipitation on post-sepsis bone marrow-derived macrophages and wound macrophages. This demonstrated decreased expression of Mll1, an epigenetic enzyme, and impaired histone 3 lysine 4 trimethylation (activation mark) at NFκB (nuclear factor kappa-light-chain-enhancer of activated B cells)-binding sites on inflammatory gene promoters in bone marrow-derived macrophages and wound macrophages from postcecal ligation and puncture mice. Bone marrow transplantation studies demonstrated epigenetic modifications initiate in bone marrow progenitor/stem cells following sepsis resulting in lasting impairment in peripheral macrophage function. Importantly, human peripheral blood leukocytes from post-septic patients demonstrate a significant reduction in MLL1 compared with nonseptic controls. CONCLUSIONS These data demonstrate that severe sepsis induces stable mixed-lineage leukemia 1-mediated epigenetic modifications in the bone marrow, which are passed to peripheral macrophages resulting in impaired macrophage function and deficient wound healing persisting long after sepsis recovery.
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Affiliation(s)
- Frank M. Davis
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI
| | - Matthew A. Schaller
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, Gainesville, FL
| | - Aaron denDekker
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI
| | - Amrita D. Joshi
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI
| | - Andrew S. Kimball
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI
| | - Holly Evanoff
- Department of Pathology, University of Michigan, Ann Arbor, MI
| | - Carol Wilke
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Andrea T. Obi
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI
| | - William J Melvin
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI
| | - Karen Cavassani
- Urological Oncology, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Melissa Scola
- Department of Pathology, University of Michigan, Ann Arbor, MI
| | - Beau Carson
- Department of Pathology, University of Michigan, Ann Arbor, MI
| | - Stephanie Moser
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI
| | - Victoria Blanc
- Biorepository Office of Research, University of Michigan, Ann Arbor, MI
| | - Milo Engoren
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI
| | - Bethany B. Moore
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI
- Department Microbiology and Immunology, University of Michigan, Ann Arbor, MI
| | | | - Katherine A. Gallagher
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI
- Department Microbiology and Immunology, University of Michigan, Ann Arbor, MI
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676
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Abstract
The role of biomarkers for detection of sepsis has come a long way. Molecular biomarkers are taking front stage at present, but machine learning and other computational measures using bigdata sets are promising. Clinical research in sepsis is hampered by lack of specificity of the diagnosis; sepsis is a syndrome with no uniformly agreed definition. This lack of diagnostic precision means there is no gold standard for this diagnosis. The final conclusion is expert opinion, which is not bad but not perfect. Perhaps machine learning will displace expert opinion as the final and most accurate definition for sepsis.
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Affiliation(s)
- Steven M Opal
- Infectious Disease Division, Alpert Medical School of Brown University, Ocean State Clinical Coordinating Center at Rhode Island Hospital, 1 Virginia Avenue Suite 105, Providence, RI 02905, USA.
| | - Xavier Wittebole
- Critical Care Department, (Pr Laterre), Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
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677
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Yoshikawa TT, Reyes BJ, Ouslander JG. Sepsis in Older Adults in Long‐Term Care Facilities: Challenges in Diagnosis and Management. J Am Geriatr Soc 2019; 67:2234-2239. [DOI: 10.1111/jgs.16194] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 08/23/2019] [Accepted: 08/26/2019] [Indexed: 02/06/2023]
Affiliation(s)
- Thomas T. Yoshikawa
- Geriatric and Extended Care Service, Department of Veterans Affairs Greater Los Angeles Healthcare System, and Department of Medicine David Geffen School of Medicine at University of California at Los Angeles Los Angeles California
| | - Bernardo J. Reyes
- Department of Integrated Medical Sciences Charles E. Schmidt College of Medicine, Florida Atlantic University Boca Raton Florida
| | - Joseph G. Ouslander
- Department of Integrated Medical Sciences Charles E. Schmidt College of Medicine, Florida Atlantic University Boca Raton Florida
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678
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Affiliation(s)
- Christopher W Seymour
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Derek C Angus
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Associate Editor
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679
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Affiliation(s)
- Jill Moser
- Department of Critical Care, University Medical Center Groningen, Groningen, the Netherlands
| | - Matijs van Meurs
- Department of Critical Care, University Medical Center Groningen, Groningen, the Netherlands
| | - Jan G Zijlstra
- Department of Critical Care, University Medical Center Groningen, Groningen, the Netherlands
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680
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Affiliation(s)
- Pope L Moseley
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Soren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
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681
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Schinkel M, Paranjape K, Nannan Panday RS, Skyttberg N, Nanayakkara PWB. Clinical applications of artificial intelligence in sepsis: A narrative review. Comput Biol Med 2019; 115:103488. [PMID: 31634699 DOI: 10.1016/j.compbiomed.2019.103488] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/25/2019] [Accepted: 10/05/2019] [Indexed: 12/27/2022]
Abstract
Many studies have been published on a variety of clinical applications of artificial intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review is to give an overview of the literature and thereby identify knowledge gaps and prioritize areas with high priority for further research. A literature search was conducted in PubMed from inception to February 2019. Search terms related to AI were combined with terms regarding sepsis. Articles were included when they reported an area under the receiver operator characteristics curve (AUROC) as outcome measure. Fifteen articles on diagnosis of sepsis with AI models were included. The best performing model reached an AUROC of 0.97. There were also seven articles on prognosis, predicting mortality over time with an AUROC of up to 0.895. Finally, there were three articles on assistance of treatment of sepsis, where the use of AI was associated with the lowest mortality rates. Of the articles, twenty-two were judged to be at high risk of bias or had major concerns regarding applicability. This was mostly because predictor variables in these models, such as blood pressure, were also part of the definition of sepsis, which led to overestimation of the performance. We conclude that AI models have great potential for improving early identification of patients who may benefit from administration of antibiotics. Current AI prediction models to diagnose sepsis are at major risks of bias when the diagnosis criteria are part of the predictor variables in the model. Furthermore, generalizability of these models is poor due to overfitting and a lack of standardized protocols for the construction and validation of the models. Until these problems have been resolved, a large gap remains between the creation of an AI algorithm and its implementation in clinical practice.
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Affiliation(s)
- M Schinkel
- Section Acute Medicine, Department of Internal Medicine, Amsterdam UMC, VU University Medical Center, De Boelelaan 1118, 1081HZ, Amsterdam, the Netherlands
| | - K Paranjape
- Section Acute Medicine, Department of Internal Medicine, Amsterdam UMC, VU University Medical Center, De Boelelaan 1118, 1081HZ, Amsterdam, the Netherlands
| | - R S Nannan Panday
- Section Acute Medicine, Department of Internal Medicine, Amsterdam UMC, VU University Medical Center, De Boelelaan 1118, 1081HZ, Amsterdam, the Netherlands
| | - N Skyttberg
- Department of Learning, Informatics, Management and Ethics, Health Informatics Centre, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - P W B Nanayakkara
- Section Acute Medicine, Department of Internal Medicine, Amsterdam UMC, VU University Medical Center, De Boelelaan 1118, 1081HZ, Amsterdam, the Netherlands.
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682
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Tosoni A, Addolorato G, Gasbarrini A, De Cosmo S, Mirijello A. Predictors of mortality of bloodstream infections among internal medicine patients: Mind the complexity of the septic population! Eur J Intern Med 2019; 68:e22-e23. [PMID: 31326194 DOI: 10.1016/j.ejim.2019.07.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 07/13/2019] [Indexed: 11/29/2022]
Affiliation(s)
- Alberto Tosoni
- Department of Internal Medicine and Hepatogastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giovanni Addolorato
- Department of Internal Medicine and Hepatogastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Antonio Gasbarrini
- Department of Internal Medicine and Hepatogastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Salvatore De Cosmo
- Department of Medical Sciences, IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, Italy
| | - Antonio Mirijello
- Department of Medical Sciences, IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, Italy.
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683
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Mathur A. What's New in Critical Illness and Injury Science? Antibiotics in critical care: Therapeutic toolbox. Int J Crit Illn Inj Sci 2019; 9:105-109. [PMID: 31620347 PMCID: PMC6792397 DOI: 10.4103/ijciis.ijciis_81_19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Anisha Mathur
- Department of Critical Care Medicine, National Institutes of Health Clinical Center, Bethesda, MD, USA
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684
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Abstract
Sepsis is a heterogeneous disease state that is both common and consequential in critically ill patients. Unfortunately, the heterogeneity of sepsis at the individual patient level has hindered advances in the field beyond the current therapeutic standards, which consist of supportive care and antibiotics. This complexity has prompted attempts to develop a precision medicine approach, with research aimed towards stratifying patients into more homogeneous cohorts with shared biological features, potentially facilitating the identification of new therapies. Several investigators have successfully utilized leukocyte-derived mRNA and discovery-based approaches to subgroup patients on the basis of biological similarities defined by transcriptomic signatures. A critical next step is to develop a consensus sepsis subclassification system, which includes transcriptomic signatures as well as other biological and clinical data. This goal will require collaboration among various investigative groups, and validation in both existing data sets and prospective studies. Such studies are required to bring precision medicine to the bedside of critically ill patients with sepsis.
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685
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Peake S, Delaney A, French CJ. Evolution not revolution: the future of the randomised controlled trial in intensive care research. Med J Aust 2019; 211:303-305.e1. [PMID: 31502278 DOI: 10.5694/mja2.50338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Sandra Peake
- The Queen Elizabeth Hospital, Adelaide, SA.,University of Adelaide, Adelaide, SA.,Monash University, Melbourne, VIC
| | - Anthony Delaney
- George Institute for Global Health, Sydney, NSW.,Royal North Shore Hospital, Sydney, NSW
| | - Craig J French
- Monash University, Melbourne, VIC.,Western Heath, Melbourne, VIC
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686
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Hasegawa D, Nishida O. Patient selection in sepsis: precision medicine using phenotypes and its implications for future clinical trial design. J Thorac Dis 2019; 11:3672-3675. [PMID: 31656636 DOI: 10.21037/jtd.2019.09.31] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Daisuke Hasegawa
- Department of Anesthesiology and Critical Care Medicine, Fujita Health University School of Medicine, Aichi, Japan
| | - Osamu Nishida
- Department of Anesthesiology and Critical Care Medicine, Fujita Health University School of Medicine, Aichi, Japan
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687
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Abstract
Biomarkers are increasingly used in patients with serious infections in the critical care setting to complement clinical judgment and interpretation of other diagnostic and prognostic tests. The main purposes of such blood markers are (1) to improve infection diagnosis (i.e., differentiation between bacterial vs. viral vs. fungal vs. noninfectious), (2) to help in the early risk stratification and thus provide prognostic information regarding the risk for mortality and other adverse outcomes, and (3) to optimize antibiotic tailoring to individual needs of patients ("antibiotic stewardship").Especially in critically ill patients, in whom sepsis is a major cause of morbidity and mortality, rapid diagnosis is desirable to start timely and specific treatment.Besides some biomarkers, such as procalcitonin, which is well established and has shown positive effects in regard to utilization of antimicrobials and clinical outcomes, there is a growing number of novel markers from different pathophysiological pathways, where the final proof of an added value to clinical judgment and ultimately clinical benefit to patients is still lacking.Without a doubt, the addition of blood biomarkers to clinical medicine has had a strong impact on the way we care for patients today. Recent trials show that as an adjunct to other clinical and laboratory parameters these markers provide important information about risks for bacterial infection and resolution of infection. Moreover, biomarkers can help to optimize management of patients with serious illness in the intensive care unit, thereby offering more individualized treatment courses with overall improvements in clinical outcomes.
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Affiliation(s)
- Eva Heilmann
- Medical University Department of Internal Medicine, Kantonsspital Aarau, Aarau, Switzerland
| | - Claudia Gregoriano
- Medical University Department of Internal Medicine, Kantonsspital Aarau, Aarau, Switzerland
| | - Philipp Schuetz
- Medical University Department of Internal Medicine, Kantonsspital Aarau, Aarau, Switzerland
- Faculty of Medicine, University of Basel, Switzerland
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688
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Vincent JL, Sakr Y. Clinical trial design for unmet clinical needs: a spotlight on sepsis. Expert Rev Clin Pharmacol 2019; 12:893-900. [DOI: 10.1080/17512433.2019.1643235] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Jean-Louis Vincent
- Dept of Intensive Care, Erasme Hospital, Université libre de Bruxelles, Brussels, Belgium
| | - Yasser Sakr
- Department of Anesthesiology and Intensive Care, Uniklinikum Jena, Jena, Germany
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689
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Knevel R, Huizinga TW. On using machine learning algorithms to define clinically meaningful patient subgroups. Ann Rheum Dis 2019; 79:e154. [PMID: 31296500 DOI: 10.1136/annrheumdis-2019-215959] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 07/04/2019] [Indexed: 01/01/2023]
Affiliation(s)
- Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tom Wj Huizinga
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
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690
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Luhr R, Cao Y, Söderquist B, Cajander S. Trends in sepsis mortality over time in randomised sepsis trials: a systematic literature review and meta-analysis of mortality in the control arm, 2002-2016. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2019; 23:241. [PMID: 31269976 PMCID: PMC6610784 DOI: 10.1186/s13054-019-2528-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 06/24/2019] [Indexed: 12/25/2022]
Abstract
Background Epidemiologic data have shown an increasing incidence and declining mortality rate in sepsis. However, confounding effects due to differences in disease classification might have contributed to these trends. To assess if a declining mortality over time could be supported by data derived from high-quality prospective studies, we performed a meta-analysis using data from randomised controlled trials (RCTs) on sepsis. The primary aim was to assess whether the mortality in sepsis trials has changed over time. The secondary aim was to investigate how many of the included trials could show efficacy of the studied intervention regarding 28-day mortality. Methods We searched PubMed for RCTs enrolling patients with severe sepsis and septic shock, published between 2002 and 2016. The included trials were assessed for quality and sorted by date of first inclusion. A meta-analysis was performed to synthesise data from the individual sepsis trials. Results Of 418 eligible articles, 44 RCTs on sepsis were included in the analysis, enrolling 13,315 patients in the usual care arm between 1991 and 2013. In this time period, mortality decreased by 0.42% annually (p = 0.04) to give a total decline of 9.24%. In subgroup analyses with adjustments for APACHE II, SAPS II and SOFA scores, the observed time trend was not significant (p = 0.45, 0.23 and 0.98 respectively). Only four of the included trials showed any efficacy with regard to mortality. Conclusions Data from RCTs show a declining trend in 28-day mortality in severe sepsis and septic shock patients during the years from 1991 to 2013. However, when controlling for severity at study inclusion, there was no significant change in mortality over time. The number of trials presenting new treatment options was low. Trial registration PROSPERO CRD42018091100. Registered 27 August 2018. Electronic supplementary material The online version of this article (10.1186/s13054-019-2528-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Robert Luhr
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 70182, Örebro, Sweden
| | - Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 70182, Örebro, Sweden.,Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Bo Söderquist
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 70182, Örebro, Sweden.,Department of Infectious Diseases, Örebro University Hospital, 70182, Örebro, Sweden
| | - Sara Cajander
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 70182, Örebro, Sweden. .,Department of Infectious Diseases, Örebro University Hospital, 70182, Örebro, Sweden.
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691
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Artificial intelligence in intensive care: are we there yet? Intensive Care Med 2019; 45:1298-1300. [DOI: 10.1007/s00134-019-05662-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Accepted: 05/07/2019] [Indexed: 10/26/2022]
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692
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Hasegawa D, Nishida O. Individualized recombinant human thrombomodulin (ART-123) administration in sepsis patients based on predicted phenotypes. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2019; 23:231. [PMID: 31234901 PMCID: PMC6591969 DOI: 10.1186/s13054-019-2521-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 06/18/2019] [Indexed: 12/16/2022]
Affiliation(s)
- Daisuke Hasegawa
- Department of Anesthesiology and Critical Care Medicine, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Osamu Nishida
- Department of Anesthesiology and Critical Care Medicine, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
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