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Titeca-Beauport D, Diouf M, Daubin D, Vong LV, Belliard G, Bruel C, Zerbib Y, Vinsonneau C, Klouche K, Maizel J. The combination of kidney function variables with cell cycle arrest biomarkers identifies distinct subphenotypes of sepsis-associated acute kidney injury: a post-hoc analysis (the PHENAKI study). Ren Fail 2024; 46:2325640. [PMID: 38445412 PMCID: PMC10919311 DOI: 10.1080/0886022x.2024.2325640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND The severity and course of sepsis-associated acute kidney injury (SA-AKI) are correlated with the mortality rate. Early detection of SA-AKI subphenotypes might facilitate the rapid provision of individualized care. PATIENTS AND METHODS In this post-hoc analysis of a multicenter prospective study, we combined conventional kidney function variables with serial measurements of urine (tissue inhibitor of metalloproteinase-2 [TIMP-2])* (insulin-like growth factor-binding protein [IGFBP7]) at 0, 6, 12, and 24 h) and then using an unsupervised hierarchical clustering of principal components (HCPC) approach to identify different phenotypes of SA-AKI. We then compared the subphenotypes with regard to a composite outcome of in-hospital death or the initiation of renal replacement therapy (RRT). RESULTS We included 184 patients presenting SA-AKI within 6 h of the initiation of catecholamines. Three distinct subphenotypes were identified: subphenotype A (99 patients) was characterized by a normal urine output (UO), a low SCr and a low [TIMP-2]*[IGFBP7] level; subphenotype B (74 patients) was characterized by existing chronic kidney disease (CKD), a higher SCr, a low UO, and an intermediate [TIMP-2]*[IGFBP7] level; and subphenotype C was characterized by very low UO, a very high [TIMP-2]*[IGFBP7] level, and an intermediate SCr level. With subphenotype A as the reference, the adjusted hazard ratio (aHR) [95%CI] for the composite outcome was 3.77 [1.92-7.42] (p < 0.001) for subphenotype B and 4.80 [1.67-13.82] (p = 0.004) for subphenotype C. CONCLUSIONS Combining conventional kidney function variables with urine measurements of [TIMP-2]*[IGFBP7] might help to identify distinct SA-AKI subphenotypes with different short-term courses and survival rates.
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Affiliation(s)
- Dimitri Titeca-Beauport
- Medical Intensive Care Unit and EA7517, Boreal Study Group, Amiens University Hospital, Amiens, France
| | - Momar Diouf
- Department of Statistics, Amiens University Hospital, Amiens, France
| | - Delphine Daubin
- Department of Intensive Care Medicine, Lapeyronie University Hospital, PhyMedExp, University of Montpellier, INSERM, CNRS, Montpellier, France
| | - Ly Van Vong
- Intensive Care Unit, Groupe Hospitalier Sud Ile de France, Melun, France
| | - Guillaume Belliard
- Medical-Surgical Intensive Care Unit, Centre Hospitalier de Bretagne Sud, Lorient, France
| | - Cédric Bruel
- Medical and Surgical Intensive Care Unit, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | - Yoann Zerbib
- Medical Intensive Care Unit and EA7517, Boreal Study Group, Amiens University Hospital, Amiens, France
| | | | - Kada Klouche
- Department of Intensive Care Medicine, Lapeyronie University Hospital, PhyMedExp, University of Montpellier, INSERM, CNRS, Montpellier, France
| | - Julien Maizel
- Medical Intensive Care Unit and EA7517, Boreal Study Group, Amiens University Hospital, Amiens, France
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Tamura H, Yasuda H, Oishi T, Shinzato Y, Amagasa S, Kashiura M, Moriya T. Association between sub-phenotypes identified using latent class analysis and neurological outcomes in patients with out-of-hospital cardiac arrest in Japan. BMC Cardiovasc Disord 2024; 24:303. [PMID: 38877462 PMCID: PMC11177357 DOI: 10.1186/s12872-024-03975-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 06/10/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND In patients who experience out-of-hospital cardiac arrest (OHCA), it is important to assess the association of sub-phenotypes identified by latent class analysis (LCA) using pre-hospital prognostic factors and factors measurable immediately after hospital arrival with neurological outcomes at 30 days, which would aid in making treatment decisions. METHODS This study retrospectively analyzed data obtained from the Japanese OHCA registry between June 2014 and December 2019. The registry included a complete set of data on adult patients with OHCA, which was used in the LCA. The association between the sub-phenotypes and 30-day survival with favorable neurological outcomes was investigated. Furthermore, adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by multivariate logistic regression analysis using in-hospital data as covariates. RESULTS A total of, 22,261 adult patients who experienced OHCA were classified into three sub-phenotypes. The factor with the highest discriminative power upon patient's arrival was Glasgow Coma Scale followed by partial pressure of oxygen. Thirty-day survival with favorable neurological outcome as the primary outcome was evident in 66.0% participants in Group 1, 5.2% in Group 2, and 0.5% in Group 3. The 30-day survival rates were 80.6%, 11.8%, and 1.3% in groups 1, 2, and 3, respectively. Logistic regression analysis revealed that the ORs (95% CI) for 30-day survival with favorable neurological outcomes were 137.1 (99.4-192.2) for Group 1 and 4.59 (3.46-6.23) for Group 2 in comparison to Group 3. For 30-day survival, the ORs (95%CI) were 161.7 (124.2-212.1) for Group 1 and 5.78 (4.78-7.04) for Group 2, compared to Group 3. CONCLUSIONS This study identified three sub-phenotypes based on the prognostic factors available immediately after hospital arrival that could predict neurological outcomes and be useful in determining the treatment strategy of patients experiencing OHCA upon their arrival at the hospital.
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Affiliation(s)
- Hiroyuki Tamura
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| | - Hideto Yasuda
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan.
| | - Takatoshi Oishi
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| | - Yutaro Shinzato
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| | - Shunsuke Amagasa
- Division of Emergency and Transport Services, National Center for Child Health and Development, Tokyo, Japan
| | - Masahiro Kashiura
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| | - Takashi Moriya
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
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Stevens J, Tezel O, Bonnefil V, Hapstack M, Atreya MR. Biological basis of critical illness subclasses: from the bedside to the bench and back again. Crit Care 2024; 28:186. [PMID: 38812006 PMCID: PMC11137966 DOI: 10.1186/s13054-024-04959-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 05/17/2024] [Indexed: 05/31/2024] Open
Abstract
Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.
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Affiliation(s)
- Joseph Stevens
- Division of Immunobiology, Graduate Program, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA
| | - Oğuzhan Tezel
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Valentina Bonnefil
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45627, USA
| | - Matthew Hapstack
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Mihir R Atreya
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45627, USA.
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Slim MA, van Amstel RBE, Bos LDJ, Cremer OL, Wiersinga WJ, van der Poll T, van Vught LA. Inflammatory subphenotypes previously identified in ARDS are associated with mortality at intensive care unit discharge: a secondary analysis of a prospective observational study. Crit Care 2024; 28:151. [PMID: 38715131 PMCID: PMC11077885 DOI: 10.1186/s13054-024-04929-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/21/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Intensive care unit (ICU)-survivors have an increased risk of mortality after discharge compared to the general population. On ICU admission subphenotypes based on the plasma biomarker levels of interleukin-8, protein C and bicarbonate have been identified in patients admitted with acute respiratory distress syndrome (ARDS) that are prognostic of outcome and predictive of treatment response. We hypothesized that if these inflammatory subphenotypes previously identified among ARDS patients are assigned at ICU discharge in a more general critically ill population, they are associated with short- and long-term outcome. METHODS A secondary analysis of a prospective observational cohort study conducted in two Dutch ICUs between 2011 and 2014 was performed. All patients discharged alive from the ICU were at ICU discharge adjudicated to the previously identified inflammatory subphenotypes applying a validated parsimonious model using variables measured median 10.6 h [IQR, 8.0-31.4] prior to ICU discharge. Subphenotype distribution at ICU discharge, clinical characteristics and outcomes were analyzed. As a sensitivity analysis, a latent class analysis (LCA) was executed for subphenotype identification based on plasma protein biomarkers at ICU discharge reflective of coagulation activation, endothelial cell activation and inflammation. Concordance between the subphenotyping strategies was studied. RESULTS Of the 8332 patients included in the original cohort, 1483 ICU-survivors had plasma biomarkers available and could be assigned to the inflammatory subphenotypes. At ICU discharge 6% (n = 86) was assigned to the hyperinflammatory and 94% (n = 1397) to the hypoinflammatory subphenotype. Patients assigned to the hyperinflammatory subphenotype were discharged with signs of more severe organ dysfunction (SOFA scores 7 [IQR 5-9] vs. 4 [IQR 2-6], p < 0.001). Mortality was higher in patients assigned to the hyperinflammatory subphenotype (30-day mortality 21% vs. 11%, p = 0.005; one-year mortality 48% vs. 28%, p < 0.001). LCA deemed 2 subphenotypes most suitable. ICU-survivors from class 1 had significantly higher mortality compared to class 2. Patients belonging to the hyperinflammatory subphenotype were mainly in class 1. CONCLUSIONS Patients assigned to the hyperinflammatory subphenotype at ICU discharge showed significantly stronger anomalies in coagulation activation, endothelial cell activation and inflammation pathways implicated in the pathogenesis of critical disease and increased mortality until one-year follow up.
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Affiliation(s)
- Marleen A Slim
- Center for Experimental and Molecular Medicine, Amsterdam University Medical Center, Amsterdam Institute for Infection and Immunity, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
- Department of Intensive Care, Amsterdam University Medical Center, Amsterdam Institute for Infection and Immunity, University of Amsterdam, Amsterdam, The Netherlands.
| | - Rombout B E van Amstel
- Department of Intensive Care, Amsterdam University Medical Center, Amsterdam Institute for Infection and Immunity, University of Amsterdam, Amsterdam, The Netherlands
| | - Lieuwe D J Bos
- Department of Intensive Care, Amsterdam University Medical Center, Amsterdam Institute for Infection and Immunity, University of Amsterdam, Amsterdam, The Netherlands
| | - Olaf L Cremer
- Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - W Joost Wiersinga
- Center for Experimental and Molecular Medicine, Amsterdam University Medical Center, Amsterdam Institute for Infection and Immunity, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Department of Medicine, Division of Infectious Diseases, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Tom van der Poll
- Center for Experimental and Molecular Medicine, Amsterdam University Medical Center, Amsterdam Institute for Infection and Immunity, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Department of Medicine, Division of Infectious Diseases, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Lonneke A van Vught
- Center for Experimental and Molecular Medicine, Amsterdam University Medical Center, Amsterdam Institute for Infection and Immunity, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Department of Intensive Care, Amsterdam University Medical Center, Amsterdam Institute for Infection and Immunity, University of Amsterdam, Amsterdam, The Netherlands
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Fang Y, Xiong B, Shang X, Yang F, Yin Y, Sun Z, Wu X, Zhang J, Liu Y. Triglyceride-glucose index predicts sepsis-associated acute kidney injury and length of stay in sepsis: A MIMIC-IV cohort study. Heliyon 2024; 10:e29257. [PMID: 38617935 PMCID: PMC11015450 DOI: 10.1016/j.heliyon.2024.e29257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 03/23/2024] [Accepted: 04/03/2024] [Indexed: 04/16/2024] Open
Abstract
Background Inflammation and stress response may be related to the occurrence of sepsis-associated acute kidney injury (SA-AKI) in patients with sepsis.Insulin resistance (IR) is closely related to the stress response, inflammatory response, immune response and severity of critical diseases. We assume that the triglyceride-glucose (TyG) index, an alternative indicator for IR, is associated with the occurrence of SA-AKI in patients with sepsis. Methods Data were obtained from The Medical Information Mart for Intensive Care-IV(MIMIC-IV) database in this retrospective cohort study. Univariate and multivariate logistic regression analysis and multivariate restricted cubic spline(RCS) regression were conducted to evaluate the association between TyG index and SA-AKI, length of stay (LOS). Subgroup and sensitivity analyses were performed to verify the robustness of the results. Results The study ultimately included data from 1426 patients with sepsis, predominantly of white ethnicity (59.2%) and male sex (56.4%), with an SA-AKI incidence rate of 78.5%. A significant linear association was observed between the TyG index and SA-AKI (OR, 1.40; 95% confidence interval(CI) [1.14-1.73]). Additionally, the TyG index demonstrated a significant correlation with the length of stay (LOS) in both the hospital (β, 1.79; 95% CI [0.80-2.77]) and the intensive care unit (ICU) (β, 1.30; 95% CI [0.80-1.79]). Subgroup and sensitivity analyses confirmed the robustness of these associations. Conclusion This study revealed a strong association between the TyG index and both SA-AKI and length of stay in patients with sepsis. These findings suggest that the TyG index is a potential predictor of SA-AKI and the length of hospitalization in sepsis cases, broadening its application in this context. However, further research is required to confirm whether interventions targeting the TyG index can genuinely enhance the clinical outcomes of patients with sepsis.
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Affiliation(s)
| | | | | | | | - Yuehao Yin
- Department of Anesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, 200032, China
| | - Zhirong Sun
- Department of Anesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, 200032, China
| | - Xin Wu
- Department of Anesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, 200032, China
| | - Jun Zhang
- Department of Anesthesiology, Shanghai Cancer Centre, Fudan University, Shanghai, 200032, China
| | - Yi Liu
- Corresponding author. Department of Anesthesiology, Shanghai Cancer Centre, Fudan University, No. 270 Dong an Road, Shanghai, 200032, China.
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Legrand M, Bagshaw SM, Bhatraju PK, Bihorac A, Caniglia E, Khanna AK, Kellum JA, Koyner J, Harhay MO, Zampieri FG, Zarbock A, Chung K, Liu K, Mehta R, Pickkers P, Ryan A, Bernholz J, Dember L, Gallagher M, Rossignol P, Ostermann M. Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials. Crit Care 2024; 28:92. [PMID: 38515121 PMCID: PMC10958912 DOI: 10.1186/s13054-024-04877-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 03/17/2024] [Indexed: 03/23/2024] Open
Abstract
Acute kidney injury (AKI) often complicates sepsis and is associated with high morbidity and mortality. In recent years, several important clinical trials have improved our understanding of sepsis-associated AKI (SA-AKI) and impacted clinical care. Advances in sub-phenotyping of sepsis and AKI and clinical trial design offer unprecedented opportunities to fill gaps in knowledge and generate better evidence for improving the outcome of critically ill patients with SA-AKI. In this manuscript, we review the recent literature of clinical trials in sepsis with focus on studies that explore SA-AKI as a primary or secondary outcome. We discuss lessons learned and potential opportunities to improve the design of clinical trials and generate actionable evidence in future research. We specifically discuss the role of enrichment strategies to target populations that are most likely to derive benefit and the importance of patient-centered clinical trial endpoints and appropriate trial designs with the aim to provide guidance in designing future trials.
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Affiliation(s)
- Matthieu Legrand
- Division of Critical Care Medicine, Department of Anesthesia and Perioperative Care, UCSF, 521 Parnassus Avenue, San Francisco, CA, 94143, USA.
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, Canada
| | - Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, USA
- Kidney Research Institute, University of Washington, Seattle, USA
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, FL, USA
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, USA
| | - Ellen Caniglia
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Ashish K Khanna
- Department of Anesthesiology, Section on Critical Care Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Outcomes Research Consortium, Cleveland, OH, USA
- Perioperative Outcomes and Informatics Collaborative, Winston-Salem, NC, USA
| | - John A Kellum
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jay Koyner
- University Section of Nephrology, Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Michael O Harhay
- Clinical Trials Methods and Outcomes Lab, Department of Biostatistics, Epidemiology, and Informatics, PAIR (Palliative and Advanced Illness Research) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fernando G Zampieri
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, Canada
| | | | | | - Kathleen Liu
- Divisions of Nephrology and Critical Care Medicine, Departments of Medicine and Anesthesia, University of California San Francisco, San Francisco, CA, USA
| | - Ravindra Mehta
- Department of Medicine, University of California, San Diego, USA
| | - Peter Pickkers
- Intensive Care Medicine, Radboudumc, Nijmegen, The Netherlands
| | - Abigail Ryan
- Chronic Care Policy Group, Division of Chronic Care Management, Center for Medicare and Medicaid Services, Center for Medicare, Baltimore, MD, USA
| | | | - Laura Dember
- Renal-Electrolyte and Hypertension Division, Department of Medicine, Department of Biostatistics, Epidemiology and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Martin Gallagher
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Patrick Rossignol
- FCRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France
- INSERM CIC-P 1433, CHRU de Nancy, INSERM U1116, Université de Lorraine, Nancy, France
- Medicine and Nephrology-Hemodialysis Departments, Monaco Private Hemodialysis Centre, Princess Grace Hospital, Monaco, Monaco
| | - Marlies Ostermann
- Department of Critical Care, King's College London, Guy's & St Thomas' Hospital, London, UK
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De Backer D, Deutschman CS, Hellman J, Myatra SN, Ostermann M, Prescott HC, Talmor D, Antonelli M, Pontes Azevedo LC, Bauer SR, Kissoon N, Loeches IM, Nunnally M, Tissieres P, Vieillard-Baron A, Coopersmith CM. Surviving Sepsis Campaign Research Priorities 2023. Crit Care Med 2024; 52:268-296. [PMID: 38240508 DOI: 10.1097/ccm.0000000000006135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
OBJECTIVES To identify research priorities in the management, epidemiology, outcome, and pathophysiology of sepsis and septic shock. DESIGN Shortly after publication of the most recent Surviving Sepsis Campaign Guidelines, the Surviving Sepsis Research Committee, a multiprofessional group of 16 international experts representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, convened virtually and iteratively developed the article and recommendations, which represents an update from the 2018 Surviving Sepsis Campaign Research Priorities. METHODS Each task force member submitted five research questions on any sepsis-related subject. Committee members then independently ranked their top three priorities from the list generated. The highest rated clinical and basic science questions were developed into the current article. RESULTS A total of 81 questions were submitted. After merging similar questions, there were 34 clinical and ten basic science research questions submitted for voting. The five top clinical priorities were as follows: 1) what is the best strategy for screening and identification of patients with sepsis, and can predictive modeling assist in real-time recognition of sepsis? 2) what causes organ injury and dysfunction in sepsis, how should it be defined, and how can it be detected? 3) how should fluid resuscitation be individualized initially and beyond? 4) what is the best vasopressor approach for treating the different phases of septic shock? and 5) can a personalized/precision medicine approach identify optimal therapies to improve patient outcomes? The five top basic science priorities were as follows: 1) How can we improve animal models so that they more closely resemble sepsis in humans? 2) What outcome variables maximize correlations between human sepsis and animal models and are therefore most appropriate to use in both? 3) How does sepsis affect the brain, and how do sepsis-induced brain alterations contribute to organ dysfunction? How does sepsis affect interactions between neural, endocrine, and immune systems? 4) How does the microbiome affect sepsis pathobiology? 5) How do genetics and epigenetics influence the development of sepsis, the course of sepsis and the response to treatments for sepsis? CONCLUSIONS Knowledge advances in multiple clinical domains have been incorporated in progressive iterations of the Surviving Sepsis Campaign guidelines, allowing for evidence-based recommendations for short- and long-term management of sepsis. However, the strength of existing evidence is modest with significant knowledge gaps and mortality from sepsis remains high. The priorities identified represent a roadmap for research in sepsis and septic shock.
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Affiliation(s)
- Daniel De Backer
- Department of Intensive Care, CHIREC Hospitals, Université Libre de Bruxelles, Brussels, Belgium
| | - Clifford S Deutschman
- Department of Pediatrics, Cohen Children's Medical Center, Northwell Health, New Hyde Park, NY
- Sepsis Research Lab, the Feinstein Institutes for Medical Research, Manhasset, NY
| | - Judith Hellman
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, CA
| | - Sheila Nainan Myatra
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Marlies Ostermann
- Department of Critical Care, King's College London, Guy's & St Thomas' Hospital, London, United Kingdom
| | - Hallie C Prescott
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Daniel Talmor
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Massimo Antonelli
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario A.Gemelli IRCCS, Rome, Italy
- Istituto di Anestesiologia e Rianimazione, Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Seth R Bauer
- Department of Pharmacy, Cleveland Clinic, Cleveland, OH
| | - Niranjan Kissoon
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Ignacio-Martin Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James's Hospital, Leinster, Dublin, Ireland
| | | | - Pierre Tissieres
- Pediatric Intensive Care, Neonatal Medicine and Pediatric Emergency, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, France
| | - Antoine Vieillard-Baron
- Service de Medecine Intensive Reanimation, Hopital Ambroise Pare, Universite Paris-Saclay, Le Kremlin-Bicêtre, France
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8
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Monard C, Bianchi N, Kelevina T, Altarelli M, Schneider A. Epidemiology and outcomes of early versus late septic acute kidney injury in critically ill patients: A retrospective cohort study. Anaesth Crit Care Pain Med 2024; 43:101332. [PMID: 38043859 DOI: 10.1016/j.accpm.2023.101332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/26/2023] [Accepted: 11/15/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND It was recently proposed to distinguish early from late sepsis-associated acute kidney injury (SA-AKI). We aimed to determine the relative frequency of these entities in critically ill patients and to describe their characteristics and outcomes. METHODS We included in this retrospective cohort study all adult patients admitted for sepsis in a tertiary ICU between 2010 and 2020. We excluded those on chronic dialysis or without consent. We extracted serum creatinine, hourly urinary output, and clinical and socio-demographic data from medical records until day 7 or ICU discharge. AKI presence and characteristics were assessed daily using KDIGO criteria. We compared patients with early (occurring within 2 days of admission) or late (occurring between day 2 and day 7) SA-AKI. We conducted sensitivity analyses using different definitions for early/late SA-AKI. RESULTS Among 1835 patients, 1660 (90%) fulfilled SA-AKI criteria. Of those, 1610 (97%) had early SA-AKI, and 50 (3%) had late SA-AKI. Similar proportions were observed when only considering AKI with elevated sCr (71% vs. 3%), severe AKI (67% vs. 6%), or different time windows for early SA-AKI. Compared with early SA-AKI patients, those with late SA-AKI were younger (median age [IQR] 59 [49-70] vs. 69 [58-76] years, p < 0.001), had lower Charlson comorbidity index (3 [1-5] vs. 5 [3-7], p < 0.001) and lower SAPSII scores (41 [34-50] vs. 53 [43-64], p < 0.001). They had similar (24% vs. 26%, p = 0.75) in-hospital mortality. CONCLUSIONS AKI is almost ubiquitous in septic critically ill patients and present within two days of admission. The timing from ICU admission might not be relevant to distinguish different phenotypes of SA-AKI. ETHICS APPROVAL Ethics Committee Vaud, Lausanne, Switzerland (n°2017-00008).
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Affiliation(s)
- Céline Monard
- Adult Intensive Care Unit, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Nathan Bianchi
- Adult Intensive Care Unit, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Tatiana Kelevina
- Adult Intensive Care Unit, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Marco Altarelli
- Adult Intensive Care Unit, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Antoine Schneider
- Adult Intensive Care Unit, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland.
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9
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Yehya N, Zinter MS, Thompson JM, Lim MJ, Hanudel MR, Alkhouli MF, Wong H, Alder MN, McKeone DJ, Halstead ES, Sinha P, Sapru A. Identification of molecular subphenotypes in two cohorts of paediatric ARDS. Thorax 2024; 79:128-134. [PMID: 37813544 PMCID: PMC10850835 DOI: 10.1136/thorax-2023-220130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 09/18/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Two subphenotypes of acute respiratory distress syndrome (ARDS), hypoinflammatory and hyperinflammatory, have been reported in adults and in a single paediatric cohort. The relevance of these subphenotypes in paediatrics requires further investigation. We aimed to identify subphenotypes in two large observational cohorts of paediatric ARDS and assess their congruence with prior descriptions. METHODS We performed latent class analysis (LCA) separately on two cohorts using biomarkers as inputs. Subphenotypes were compared on clinical characteristics and outcomes. Finally, we assessed overlap with adult cohorts using parsimonious classifiers. FINDINGS In two cohorts from the Children's Hospital of Philadelphia (n=333) and from a multicentre study based at the University of California San Francisco (n=293), LCA identified two subphenotypes defined by differential elevation of biomarkers reflecting inflammation and endotheliopathy. In both cohorts, hyperinflammatory subjects had greater illness severity, more sepsis and higher mortality (41% and 28% in hyperinflammatory vs 11% and 7% in hypoinflammatory). Both cohorts demonstrated overlap with adult subphenotypes when assessed using parsimonious classifiers. INTERPRETATION We identified hypoinflammatory and hyperinflammatory subphenotypes of paediatric ARDS from two separate cohorts with utility for prognostic and potentially predictive, enrichment. Future paediatric ARDS trials should identify and leverage biomarker-defined subphenotypes in their analysis.
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Affiliation(s)
- Nadir Yehya
- Division of Pediatric Critical Care, Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA
| | - Matt S Zinter
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
- Division of Allergy, Immunology, and Bone Marrow Transplantation, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Jill M Thompson
- Division of Pediatric Critical Care, Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA
| | - Michelle J Lim
- Department of Pediatrics, UC Davis, Davis, California, USA
| | - Mark R Hanudel
- Department of Pediatrics, University of California Los Angeles, Los Angeles, California, USA
| | - Mustafa F Alkhouli
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Hector Wong
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Matthew N Alder
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Daniel J McKeone
- Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - E Scott Halstead
- Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Pratik Sinha
- Division of Clinical and Translational Research, Washington University School of Medicine, St. Louis, MO, USA
- Division of Critical Care, Department of Anesthesia, Washington University, St. Louis, MO, USA
| | - Anil Sapru
- Department of Pediatrics, University of California Los Angeles, Los Angeles, California, USA
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10
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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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11
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Cox EGM, Zhang W, van der Voort PHJ, Lunter G, Keus F, Snieder H. Genetic association studies in critically ill patients: protocol for a systematic review. Syst Rev 2023; 12:233. [PMID: 38093336 PMCID: PMC10716946 DOI: 10.1186/s13643-023-02401-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 11/23/2023] [Indexed: 12/17/2023] Open
Abstract
INTRODUCTION Patients in the intensive care unit (ICU) are highly heterogeneous in characteristics, their clinical course, and outcomes. Genetic variability may partly explain the variability and similarity in disease courses observed among critically ill patients and may identify clusters of subgroups. The aim of this study is to conduct a systematic review of all genetic association studies of critically ill patients with their outcomes. METHODS AND ANALYSIS This systematic review will be conducted and reported according to the HuGE Review Handbook V1.0. We will search PubMed, Embase, and the Cochrane Library for relevant studies. All types of genetic association studies that included acutely admitted medical and surgical adult ICU patients will be considered for this review. All studies will be selected according to predefined selection criteria, evaluated and assessed for risk of bias independently by two reviewers. Risk of bias will be assessed according to the HuGE Review Handbook V1.0 with some modifications reflecting recent insights. We will provide an overview of all included studies by reporting the characteristics of the study designs, the patients included in the studies, the genetic variables, and the outcomes evaluated. ETHICS AND DISSEMINATION We will use data from peer-reviewed published articles, and hence, there is no requirement for ethics approval. The results of this systematic review will be disseminated through publication in a peer-reviewed scientific journal. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42021209744.
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Affiliation(s)
- Eline G M Cox
- Department of Critical Care, University Medical Center Groningen, Groningen, 9713 GZ, the Netherlands.
| | - Wenbo Zhang
- Department of Epidemiology, University Medical Center Groningen, Groningen, 9713 GZ, the Netherlands
| | - Peter H J van der Voort
- Department of Critical Care, University Medical Center Groningen, Groningen, 9713 GZ, the Netherlands
| | - Gerton Lunter
- Department of Epidemiology, University Medical Center Groningen, Groningen, 9713 GZ, the Netherlands
| | - Frederik Keus
- Department of Critical Care, University Medical Center Groningen, Groningen, 9713 GZ, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University Medical Center Groningen, Groningen, 9713 GZ, the Netherlands
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12
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Lai CF, Liu JH, Tseng LJ, Tsao CH, Chou NK, Lin SL, Chen YM, Wu VC. Unsupervised clustering identifies sub-phenotypes and reveals novel outcome predictors in patients with dialysis-requiring sepsis-associated acute kidney injury. Ann Med 2023; 55:2197290. [PMID: 37043222 PMCID: PMC10101673 DOI: 10.1080/07853890.2023.2197290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 03/25/2023] [Indexed: 04/13/2023] Open
Abstract
INTRODUCTION Heterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This study aimed to perform unsupervised consensus clustering in critically ill patients with dialysis-requiring SA-AKI. PATIENTS AND METHODS This prospective observational cohort study included all septic patients, defined by the Sepsis-3 criteria, with dialysis-requiring SA-AKI in surgical intensive care units in Taiwan between 2009 and 2018. We employed unsupervised consensus clustering based on 23 clinical variables upon initializing renal replacement therapy. Multivariate-adjusted Cox regression models and Fine-Gray sub-distribution hazard models were built to test associations between cluster memberships with mortality and being free of dialysis at 90 days after hospital discharge, respectively. RESULTS Consensus clustering among 999 enrolled patients identified three sub-phenotypes characterized with distinct clinical manifestations upon renal replacement therapy initiation (n = 352, 396 and 251 in cluster 1, 2 and 3, respectively). They were followed for a median of 48 (interquartile range 9.5-128.5) days. Phenotypic cluster 1, featured by younger age, lower Charlson Comorbidity Index, higher baseline estimated glomerular filtration rate but with higher severity of acute illness was associated with an increased risk of death (adjusted hazard ratio of 3.05 [95% CI, 2.35-3.97]) and less probability to become free of dialysis (adjusted sub-distribution hazard ratio of 0.55 [95% CI, 0.38-0.8]) than cluster 3. By examining distinct features of the sub-phenotypes, we discovered that pre-dialysis hyperlactatemia ≥3.3 mmol/L was an independent outcome predictor. A clinical model developed to determine high-risk sub-phenotype 1 in this cohort (C-static 0.99) can identify a sub-phenotype with high in-hospital mortality risk (adjusted hazard ratio of 1.48 [95% CI, 1.25-1.74]) in another independent multi-centre SA-AKI cohort. CONCLUSIONS Our data-driven approach suggests sub-phenotypes with clinical relevance in dialysis-requiring SA-AKI and serves an outcome predictor. This strategy represents further development toward precision medicine in the definition of high-risk sub-phenotype in patients with SA-AKI.Key messagesUnsupervised consensus clustering can identify sub-phenotypes of patients with SA-AKI and provide a risk prediction.Examining the features of patient heterogeneity contributes to the discovery of serum lactate levels ≥ 3.3 mmol/L upon initializing RRT as an independent outcome predictor.This data-driven approach can be useful for prognostication and lead to a better understanding of therapeutic strategies in heterogeneous clinical syndromes.
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Affiliation(s)
- Chun-Fu Lai
- Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
| | - Jung-Hua Liu
- Department of Communication, National Chung Cheng University, Minhsiung, Taiwan
| | - Li-Jung Tseng
- Department of Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Chun-Hao Tsao
- Department of Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Nai-Kuan Chou
- Department of Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Shuei-Liong Lin
- Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
- Graduate Institute of Physiology, National Taiwan University College of Medicine, Taipei City, Taiwan
| | - Yung-Ming Chen
- Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
- National Taiwan University Hospital Bei-Hu Branch, Taipei City, Taiwan
| | - Vin-Cent Wu
- Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
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13
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Hartman E, Scott AM, Karlsson C, Mohanty T, Vaara ST, Linder A, Malmström L, Malmström J. Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis. Nat Commun 2023; 14:5359. [PMID: 37660105 PMCID: PMC10475049 DOI: 10.1038/s41467-023-41146-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/22/2023] [Indexed: 09/04/2023] Open
Abstract
The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).
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Affiliation(s)
- Erik Hartman
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden.
| | - Aaron M Scott
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Christofer Karlsson
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Tirthankar Mohanty
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Suvi T Vaara
- Department of Perioperative and Intensive Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Adam Linder
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Lars Malmström
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden.
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14
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Bhatraju PK, Prince DK, Mansour S, Ikizler TA, Siew ED, Chinchilli VM, Garg AX, Go AS, Kaufman JS, Kimmel PL, Coca SG, Parikh CR, Wurfel MM, Himmelfarb J. Integrated Analysis of Blood and Urine Biomarkers to Identify Acute Kidney Injury Subphenotypes and Associations With Long-term Outcomes. Am J Kidney Dis 2023; 82:311-321.e1. [PMID: 37178093 PMCID: PMC10523857 DOI: 10.1053/j.ajkd.2023.01.449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/15/2023] [Indexed: 05/15/2023]
Abstract
RATIONALE & OBJECTIVE Acute kidney injury (AKI) is a heterogeneous clinical syndrome with varying causes, pathophysiology, and outcomes. We incorporated plasma and urine biomarker measurements to identify AKI subgroups (subphenotypes) more tightly linked to underlying pathophysiology and long-term clinical outcomes. STUDY DESIGN Multicenter cohort study. SETTING & PARTICIPANTS 769 hospitalized adults with AKI matched with 769 without AKI, enrolled from December 2009 to February 2015 in the ASSESS-AKI Study. PREDICTORS 29 clinical, plasma, and urinary biomarker parameters used to identify AKI subphenotypes. OUTCOME Composite of major adverse kidney events (MAKE) with a median follow-up period of 4.7 years. ANALYTICAL APPROACH Latent class analysis (LCA) and k-means clustering were applied to 29 clinical, plasma, and urinary biomarker parameters. Associations between AKI subphenotypes and MAKE were analyzed using Kaplan-Meier curves and Cox proportional hazard models. RESULTS Among 769 AKI patients both LCA and k-means identified 2 distinct AKI subphenotypes (classes 1 and 2). The long-term risk for MAKE was higher with class 2 (adjusted HR, 1.41 [95% CI, 1.08-1.84]; P=0.01) compared with class 1, adjusting for demographics, hospital level factors, and KDIGO stage of AKI. The higher risk of MAKE among class 2 was explained by a higher risk of long-term chronic kidney disease progression and dialysis. The top variables that were different between classes 1 and 2 included plasma and urinary biomarkers of inflammation and epithelial cell injury; serum creatinine ranked 20th out of the 29 variables for differentiating classes. LIMITATIONS A replication cohort with simultaneously collected blood and urine sampling in hospitalized adults with AKI and long-term outcomes was unavailable. CONCLUSIONS We identify 2 molecularly distinct AKI subphenotypes with differing risk of long-term outcomes, independent of the current criteria to risk stratify AKI. Future identification of AKI subphenotypes may facilitate linking therapies to underlying pathophysiology to prevent long-term sequalae after AKI. PLAIN-LANGUAGE SUMMARY Acute kidney injury (AKI) occurs commonly in hospitalized patients and is associated with high morbidity and mortality. The AKI definition lumps many different types of AKI together, but subgroups of AKI may be more tightly linked to the underlying biology and clinical outcomes. We used 29 different clinical, blood, and urinary biomarkers and applied 2 different statistical algorithms to identify AKI subtypes and their association with long-term outcomes. Both clustering algorithms identified 2 AKI subtypes with different risk of chronic kidney disease, independent of the serum creatinine concentrations (the current gold standard to determine severity of AKI). Identification of AKI subtypes may facilitate linking therapies to underlying biology to prevent long-term consequences after AKI.
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Affiliation(s)
- Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington; Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington.
| | - David K Prince
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Sherry Mansour
- Division of Nephrology, Yale University, New Haven, Connecticut
| | - T Alp Ikizler
- Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Edward D Siew
- Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Vernon M Chinchilli
- Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania
| | - Amit X Garg
- Division of Nephrology, Department of Medicine, Western University, London, Ontario, Canada
| | - Alan S Go
- Division of Nephrology, Department of Medicine, University of California, San Francisco, California; Department of Epidemiology and Biostatistics, University of California, San Francisco, California; Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - James S Kaufman
- Division of Nephrology, School of Medicine, New York University, New York, New York; Division of Nephrology, VA New York Harbor Healthcare System, New York, New York
| | - Paul L Kimmel
- National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Steve G Coca
- Section of Nephrology, Department of Internal Medicine, Mount Sinai School of Medicine, New York, New York
| | - Chirag R Parikh
- Division of Nephrology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Mark M Wurfel
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington; Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Jonathan Himmelfarb
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
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15
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Stanski NL, Rodrigues CE, Strader M, Murray PT, Endre ZH, Bagshaw SM. Precision management of acute kidney injury in the intensive care unit: current state of the art. Intensive Care Med 2023; 49:1049-1061. [PMID: 37552332 DOI: 10.1007/s00134-023-07171-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/12/2023] [Indexed: 08/09/2023]
Abstract
Acute kidney injury (AKI) is a prototypical example of a common syndrome in critical illness defined by consensus. The consensus definition for AKI, traditionally defined using only serum creatinine and urine output, was needed to standardize the description for epidemiology and to harmonize eligibility for clinical trials. However, AKI is not a simple disease, but rather a complex and multi-factorial syndrome characterized by a wide spectrum of pathobiology. AKI is now recognized to be comprised of numerous sub-phenotypes that can be discriminated through shared features such as etiology, prognosis, or common pathobiological mechanisms of injury and damage. The characterization of sub-phenotypes can serve to enable prognostic enrichment (i.e., identify subsets of patients more likely to share an outcome of interest) and predictive enrichment (identify subsets of patients more likely to respond favorably to a given therapy). Existing and emerging biomarkers will aid in discriminating sub-phenotypes of AKI, facilitate expansion of diagnostic criteria, and be leveraged to realize personalized approaches to management, particularly for recognizing treatment-responsive mechanisms (i.e., endotypes) and targets for intervention (i.e., treatable traits). Specific biomarkers (e.g., serum renin; olfactomedin 4 (OLFM4); interleukin (IL)-9) may further enable identification of pathobiological mechanisms to serve as treatment targets. However, even non-specific biomarkers of kidney injury (e.g., neutrophil gelatinase-associated lipocalin, NGAL; [tissue inhibitor of metalloproteinases 2, TIMP2]·[insulin like growth factor binding protein 7, IGFBP7]; kidney injury molecule 1, KIM-1) can direct greater precision management for specific sub-phenotypes of AKI. This review will summarize these evolving concepts and recent innovations in precision medicine approaches to the syndrome of AKI in critical illness, along with providing examples of how they can be leveraged to guide patient care.
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Affiliation(s)
- Natalja L Stanski
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Camila E Rodrigues
- Department of Nephrology, Prince of Wales Clinical School, UNSW Medicine, Sydney, NSW, Australia
- Nephrology Department, Hospital das Clínicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Michael Strader
- Department of Medicine, School of Medicine, University College Dublin, Dublin, Ireland
| | - Patrick T Murray
- Department of Medicine, School of Medicine, University College Dublin, Dublin, Ireland
| | - Zoltan H Endre
- Department of Nephrology, Prince of Wales Clinical School, UNSW Medicine, Sydney, NSW, Australia
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, 2-124 Clinical Sciences Building, 8440-112 ST NW, Edmonton, AB, T6G 2B7, Canada.
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16
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Papathanakos G, Andrianopoulos I, Xenikakis M, Papathanasiou A, Koulenti D, Blot S, Koulouras V. Clinical Sepsis Phenotypes in Critically Ill Patients. Microorganisms 2023; 11:2165. [PMID: 37764009 PMCID: PMC10538192 DOI: 10.3390/microorganisms11092165] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/10/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.
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Affiliation(s)
- Georgios Papathanakos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Ioannis Andrianopoulos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Menelaos Xenikakis
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Athanasios Papathanasiou
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Despoina Koulenti
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QL 4029, Australia;
- Second Critical Care Department, Attikon University Hospital, Rimini Street, 12462 Athens, Greece
| | - Stijn Blot
- Department of Internal Medicine & Pediatrics, Ghent University, 9000 Ghent, Belgium;
| | - Vasilios Koulouras
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
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17
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Atreya MR, Cvijanovich NZ, Fitzgerald JC, Weiss SL, Bigham MT, Jain PN, Schwarz AJ, Lutfi R, Nowak J, Allen GL, Thomas NJ, Grunwell JR, Baines T, Quasney M, Haileselassie B, Alder MN, Goldstein SL, Stanski NL. Prognostic and predictive value of endothelial dysfunction biomarkers in sepsis-associated acute kidney injury: risk-stratified analysis from a prospective observational cohort of pediatric septic shock. Crit Care 2023; 27:260. [PMID: 37400882 PMCID: PMC10318688 DOI: 10.1186/s13054-023-04554-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 06/28/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Sepsis-associated acute kidney injury (SA-AKI) is associated with high morbidity, with no current therapies available beyond continuous renal replacement therapy (CRRT). Systemic inflammation and endothelial dysfunction are key drivers of SA-AKI. We sought to measure differences between endothelial dysfunction markers among children with and without SA-AKI, test whether this association varied across inflammatory biomarker-based risk strata, and develop prediction models to identify those at highest risk of SA-AKI. METHODS Secondary analyses of prospective observational cohort of pediatric septic shock. Primary outcome of interest was the presence of ≥ Stage II KDIGO SA-AKI on day 3 based on serum creatinine (D3 SA-AKI SCr). Biomarkers including those prospectively validated to predict pediatric sepsis mortality (PERSEVERE-II) were measured in Day 1 (D1) serum. Multivariable regression was used to test the independent association between endothelial markers and D3 SA-AKI SCr. We conducted risk-stratified analyses and developed prediction models using Classification and Regression Tree (CART), to estimate risk of D3 SA-AKI among prespecified subgroups based on PERSEVERE-II risk. RESULTS A total of 414 patients were included in the derivation cohort. Patients with D3 SA-AKI SCr had worse clinical outcomes including 28-day mortality and need for CRRT. Serum soluble thrombomodulin (sTM), Angiopoietin-2 (Angpt-2), and Tie-2 were independently associated with D3 SA-AKI SCr. Further, Tie-2 and Angpt-2/Tie-2 ratios were influenced by the interaction between D3 SA-AKI SCr and risk strata. Logistic regression demonstrated models predictive of D3 SA-AKI risk performed optimally among patients with high- or intermediate-PERSEVERE-II risk strata. A 6 terminal node CART model restricted to this subgroup of patients had an area under the receiver operating characteristic curve (AUROC) 0.90 and 0.77 upon tenfold cross-validation in the derivation cohort to distinguish those with and without D3 SA-AKI SCr and high specificity. The newly derived model performed modestly in a unique set of patients (n = 224), 84 of whom were deemed high- or intermediate-PERSEVERE-II risk, to distinguish those patients with high versus low risk of D3 SA-AKI SCr. CONCLUSIONS Endothelial dysfunction biomarkers are independently associated with risk of severe SA-AKI. Pending validation, incorporation of endothelial biomarkers may facilitate prognostic and predictive enrichment for selection of therapeutics in future clinical trials among critically ill children.
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Affiliation(s)
- Mihir R Atreya
- Division of Critical Care Medicine, MLC2005, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA.
| | | | | | - Scott L Weiss
- Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | | | - Parag N Jain
- Texas Children's Hospital, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Adam J Schwarz
- Children's Hospital of Orange County, Orange, CA, 92868, USA
| | - Riad Lutfi
- Riley Hospital for Children, Indianapolis, IN, 46202, USA
| | - Jeffrey Nowak
- Children's Hospital and Clinics of Minnesota, Minneapolis, MN, 55404, USA
| | | | - Neal J Thomas
- Penn State Hershey Children's Hospital, Hershey, PA, 17033, USA
| | | | - Torrey Baines
- University of Florida Health Shands Children's Hospital, Gainesville, FL, 32610, USA
| | - Michael Quasney
- CS Mott Children's Hospital at the University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Matthew N Alder
- Division of Critical Care Medicine, MLC2005, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
| | - Stuart L Goldstein
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
- Division of Nephrology, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, OH, 45229, USA
| | - Natalja L Stanski
- Division of Critical Care Medicine, MLC2005, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
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18
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Bhatraju PK, Stanaway IB, Palmer MR, Menon R, Schaub JA, Menez S, Srivastava A, Wilson FP, Kiryluk K, Palevsky PM, Naik AS, Sakr SS, Jarvik GP, Parikh CR, Ware LB, Ikizler TA, Siew ED, Chinchilli VM, Coca SG, Garg AX, Go AS, Kaufman JS, Kimmel PL, Himmelfarb J, Wurfel MM. Genome-wide Association Study for AKI. KIDNEY360 2023; 4:870-880. [PMID: 37273234 PMCID: PMC10371295 DOI: 10.34067/kid.0000000000000175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/03/2023] [Indexed: 06/06/2023]
Abstract
Key Points Two genetic variants in the DISP1-TLR5 gene locus were associated with risk of AKI. DISP1 and TLR5 were differentially regulated in kidney biopsy tissue from patients with AKI compared with no AKI. Background Although common genetic risks for CKD are well established, genetic factors influencing risk for AKI in hospitalized patients are poorly understood. Methods We conducted a genome-wide association study in 1369 participants in the Assessment, Serial Evaluation, and Subsequent Sequelae of AKI Study; a multiethnic population of hospitalized participants with and without AKI matched on demographics, comorbidities, and kidney function before hospitalization. We then completed functional annotation of top-performing variants for AKI using single-cell RNA sequencing data from kidney biopsies in 12 patients with AKI and 18 healthy living donors from the Kidney Precision Medicine Project. Results No genome-wide significant associations with AKI risk were found in Assessment, Serial Evaluation, and Subsequent Sequelae of AKI (P < 5×10 −8 ). The top two variants with the strongest association with AKI mapped to the dispatched resistance-nodulation-division (RND) transporter family member 1 (DISP1) gene and toll-like receptor 5 (TLR5) gene locus, rs17538288 (odds ratio, 1.55; 95% confidence interval, 1.32 to 182; P = 9.47×10 −8 ) and rs7546189 (odds ratio, 1.53; 95% confidence interval, 1.30 to 1.81; P = 4.60×10 −7 ). In comparison with kidney tissue from healthy living donors, kidney biopsies in patients with AKI showed differential DISP1 expression in proximal tubular epithelial cells (adjusted P = 3.9× 10−2) and thick ascending limb of the loop of Henle (adjusted P = 8.7× 10−3) and differential TLR5 gene expression in thick ascending limb of the loop of Henle (adjusted P = 4.9× 10−30). Conclusions AKI is a heterogeneous clinical syndrome with various underlying risk factors, etiologies, and pathophysiology that may limit the identification of genetic variants. Although no variants reached genome-wide significance, we report two variants in the intergenic region between DISP1 and TLR5 , suggesting this region as a novel risk for AKI susceptibility.
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Affiliation(s)
- Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Ian B Stanaway
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Melody R Palmer
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Rajasree Menon
- Division of Nephrology, Department of Medicine, Michigan Medicine, Ann Arbor, Michigan
| | - Jennifer A Schaub
- Division of Nephrology, Department of Medicine, Michigan Medicine, Ann Arbor, Michigan
| | - Steven Menez
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Anand Srivastava
- Department of Medicine, Division of Nephrology and Hypertension, Northwestern University School of Medicine, Chicago, Illinois
| | - F Perry Wilson
- Program of Applied Translational Research, Yale School of Medicine, New Haven, Connecticut
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York City, New York
| | - Paul M Palevsky
- Kidney Medicine Section, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Renal-Electrolyte Division, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Abhijit S Naik
- Division of Nephrology, University of Michigan, Ann Arbor, Michigan
| | - Sana S Sakr
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Chirag R Parikh
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Lorraine B Ware
- Division of Allergy, Pulmonary and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee
| | - T Alp Ikizler
- Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Edward D Siew
- Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Vernon M Chinchilli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania
| | - Steve G Coca
- Section of Nephrology, Department of Internal Medicine, Mount Sinai School of Medicine, New York, New York
| | - Amit X Garg
- Division of Nephrology, Department of Medicine, Western University, London, Ontario, Canada
| | - Alan S Go
- Division of Nephrology, Department of Medicine, University of California, San Francisco, California
- Division of Research, Kaiser Permanente Northern California, Oakland, California
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California
| | - James S Kaufman
- Division of Nephrology, New York University School of Medicine, New York, New York
- Division of Nephrology, VA New York Harbor Healthcare System, New York, New York
| | - Paul L Kimmel
- Division of Renal Diseases and Hypertension, Department of Medicine, George Washington University Medical Center, Washington, DC
| | - Jonathan Himmelfarb
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Mark M Wurfel
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
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19
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Wu X, Zhai F, Chang A, Wei J, Guo Y, Zhang J. Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study. Diabetes Metab Syndr Obes 2023; 16:1987-2003. [PMID: 37408729 PMCID: PMC10319347 DOI: 10.2147/dmso.s406695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/22/2023] [Indexed: 07/07/2023] Open
Abstract
Purpose Diagnosing osteoporosis in T2DM based on bone mineral density (BMD) remains challenging. We sought to develop prediction models employing machine learning algorithms for use as screening instruments for osteoporosis in T2DM patients. Patients and Methods Data were collected from 433 participants and analyzed using nine categorical machine learning algorithms to select features based on demographic and clinical variables. Multiple classification models were compared using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, the average precision (AP), precision, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) to determine the best model. In addition, 5-fold cross-validation was utilized to optimize the model, followed by an evaluation of feature significance using Shapley Additive exPlanations (SHAP). Using latent class analysis (LCA), distinct subpopulations were identified by constructing several discrete clusters. Results In this study, nine feature variables were identified to construct predictive models for osteoporosis in individuals with T2DM. The machine learning algorithms achieved an AP range of 0.444-1.000. The XGBoost model was selected as the final prediction model with an AUROC of 0.940 in the training set, 0.772 in the validation set for 5-fold cross-validation, and 0.872 in the test set. Using SHAP methodology, 25(OH)D was identified as the most important risk factor. Additionally, a 3-Class model was constructed using LCA, which categorized individuals into high, medium, and low-risk groups. Conclusion Our study developed a predictive model with high accuracy and clinical validity for predicting osteoporosis in type 2 diabetes patients. We also identified three subpopulations with varying osteoporosis risk using clustering. However, limited sample size warrants cautious interpretation of results, and validation in larger cohorts is needed.
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Affiliation(s)
- Xuelun Wu
- Department of Endocrinology, Cangzhou Central Hospital, Cangzhou City, Hebei Province, People’s Republic of China
| | - Furui Zhai
- Gynecological Clinic, Cangzhou Central Hospital, Cangzhou City, Hebei Province, People’s Republic of China
| | - Ailing Chang
- Department of Endocrinology, Cangzhou Central Hospital, Cangzhou City, Hebei Province, People’s Republic of China
| | - Jing Wei
- Department of Endocrinology, Cangzhou Central Hospital, Cangzhou City, Hebei Province, People’s Republic of China
| | - Yanan Guo
- Department of Endocrinology, Cangzhou Central Hospital, Cangzhou City, Hebei Province, People’s Republic of China
| | - Jincheng Zhang
- Department of Endocrinology, Cangzhou Central Hospital, Cangzhou City, Hebei Province, People’s Republic of China
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20
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Scott AM, Karlsson C, Mohanty T, Hartman E, Vaara ST, Linder A, Malmström J, Malmström L. Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics. Commun Biol 2023; 6:628. [PMID: 37301900 PMCID: PMC10257694 DOI: 10.1038/s42003-023-04977-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics.
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Affiliation(s)
- Aaron M Scott
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden.
| | - Christofer Karlsson
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Tirthankar Mohanty
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Erik Hartman
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Suvi T Vaara
- Division of Anaesthesia and Intensive Care Medicine Department of Surgery, Intensive Care Units, Helsinki University Central Hospital, Box 340, 00029 HUS, Helsinki, Finland
| | - Adam Linder
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Lars Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden.
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21
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Chotalia M, Patel JM, Bangash MN, Parekh D. Cardiovascular Subphenotypes in ARDS: Diagnostic and Therapeutic Implications and Overlap with Other ARDS Subphenotypes. J Clin Med 2023; 12:jcm12113695. [PMID: 37297890 DOI: 10.3390/jcm12113695] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/27/2023] [Accepted: 05/15/2023] [Indexed: 06/12/2023] Open
Abstract
Acute respiratory distress syndrome (ARDS) is a highly heterogeneous clinical condition. Shock is a poor prognostic sign in ARDS, and heterogeneity in its pathophysiology may be a barrier to its effective treatment. Although right ventricular dysfunction is commonly implicated, there is no consensus definition for its diagnosis, and left ventricular function is neglected. There is a need to identify the homogenous subgroups within ARDS, that have a similar pathobiology, which can then be treated with targeted therapies. Haemodynamic clustering analyses in patients with ARDS have identified two subphenotypes of increasingly severe right ventricular injury, and a further subphenotype of hyperdynamic left ventricular function. In this review, we discuss how phenotyping the cardiovascular system in ARDS may align with haemodynamic pathophysiology, can aid in optimally defining right ventricular dysfunction and can identify tailored therapeutic targets for shock in ARDS. Additionally, clustering analyses of inflammatory, clinical and radiographic data describe other subphenotypes in ARDS. We detail the potential overlap between these and the cardiovascular phenotypes.
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Affiliation(s)
- Minesh Chotalia
- Birmingham Acute Care Research Group, University of Birmingham, Birmingham B15 2SQ, UK
- Department of Anaesthetics and Critical Care, Queen Elizabeth Hospital Birmingham, Birmingham B15 2GW, UK
| | - Jaimin M Patel
- Birmingham Acute Care Research Group, University of Birmingham, Birmingham B15 2SQ, UK
- Department of Anaesthetics and Critical Care, Queen Elizabeth Hospital Birmingham, Birmingham B15 2GW, UK
| | - Mansoor N Bangash
- Birmingham Acute Care Research Group, University of Birmingham, Birmingham B15 2SQ, UK
- Department of Anaesthetics and Critical Care, Queen Elizabeth Hospital Birmingham, Birmingham B15 2GW, UK
| | - Dhruv Parekh
- Birmingham Acute Care Research Group, University of Birmingham, Birmingham B15 2SQ, UK
- Department of Anaesthetics and Critical Care, Queen Elizabeth Hospital Birmingham, Birmingham B15 2GW, UK
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22
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Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol 2023; 19:401-417. [PMID: 36823168 DOI: 10.1038/s41581-023-00683-3] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/18/2023] [Indexed: 02/25/2023]
Abstract
Sepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients and is strongly associated with adverse outcomes, including an increased risk of chronic kidney disease, cardiovascular events and death. The pathophysiology of SA-AKI remains elusive, although microcirculatory dysfunction, cellular metabolic reprogramming and dysregulated inflammatory responses have been implicated in preclinical studies. SA-AKI is best defined as the occurrence of AKI within 7 days of sepsis onset (diagnosed according to Kidney Disease Improving Global Outcome criteria and Sepsis 3 criteria, respectively). Improving outcomes in SA-AKI is challenging, as patients can present with either clinical or subclinical AKI. Early identification of patients at risk of AKI, or at risk of progressing to severe and/or persistent AKI, is crucial to the timely initiation of adequate supportive measures, including limiting further insults to the kidney. Accordingly, the discovery of biomarkers associated with AKI that can aid in early diagnosis is an area of intensive investigation. Additionally, high-quality evidence on best-practice care of patients with AKI, sepsis and SA-AKI has continued to accrue. Although specific therapeutic options are limited, several clinical trials have evaluated the use of care bundles and extracorporeal techniques as potential therapeutic approaches. Here we provide graded recommendations for managing SA-AKI and highlight priorities for future research.
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23
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Ostermann M, Basu RK, Mehta RL. Acute kidney injury. Intensive Care Med 2023; 49:219-222. [PMID: 36592201 DOI: 10.1007/s00134-022-06946-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 11/28/2022] [Indexed: 01/03/2023]
Affiliation(s)
- Marlies Ostermann
- Department of Critical Care, King's College London, Guy's & St Thomas' NHS Foundation Hospital, London, SE1 7EH, UK.
| | - Rajit K Basu
- Ann & Robert Lurie Children's Hospital of Chicago, Northwestern University, Chicago, IL, USA
| | - Ravindra L Mehta
- University of California, San Diego Health Sciences, San Diego, CA, USA
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24
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Abstract
Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.
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Affiliation(s)
- Pratik Sinha
- Division of Clinical & Translational Research and Division of Critical Care, Department of Anesthesia, Washington University, St. Louis, Missouri, USA;
| | - Nuala J Meyer
- Division of Pulmonary, Allergy, and Critical Care Medicine; Center for Translational Lung Biology; and Lung Biology Institute, University of Pennsylvania Perelman School of Medicine; Philadelphia, Pennsylvania, USA
| | - Carolyn S Calfee
- Division of Pulmonary, Critical Care, Allergy & Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, USA
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25
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Hu J, Ananth D, Sethi SK, Taliwal N, Govindan S, Raina R. Neonatal AKI: An update. J Neonatal Perinatal Med 2023; 16:361-373. [PMID: 37718869 DOI: 10.3233/npm-230120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Neonatal acute kidney injury (AKI) is a common complication, especially in the neonatal intensive care unit, that is associated with long term consequences and poor outcomes. Early detection and treatment is critical. Currently, neonatal AKI is defined with urinary markers and serum creatinine, with limitations on early detection and individual treatment. There have been numerous biomarkers and risk factor scores that have been studied for their ability to predict neonatal AKI. To move towards personalized medicine, neonatal AKI must be categorized into phenotypes and subphenotypes that fully encapsulate the diverse causes and specific treatments. This review aims to advance our understanding of neonatal AKI detection through the use of biomarkers, subphenotypes, and phenotypes to move towards personalized treatment strategies.
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Affiliation(s)
- J Hu
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - D Ananth
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - S K Sethi
- Pediatric Nephrology & Pediatric Kidney Transplantation, Kidney and Urology Institute, Medanta, The Medicity Hospital, Gurgaon, India
| | - N Taliwal
- Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA
| | - S Govindan
- Department of Pediatric Nephrology, Dr. Mehta's Hospitals, Chetpet and Vellapanchavadi, Chennai, India
| | - R Raina
- Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA
- Department of Nephrology, Akron Children's Hospital, Akron, OH, USA
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Zhang Z, Chen L, Liu H, Sun Y, Shui P, Gao J, Wang D, Jiang H, Li Y, Chen K, Hong Y. Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury. Crit Care 2022; 26:398. [PMID: 36544199 PMCID: PMC9773539 DOI: 10.1186/s13054-022-04234-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized. METHODS Sepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFArenal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI. RESULTS A total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFArenal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFArenal > 0 on Days 1 and 3, n = 62), transient AKI (SOFArenal > 0 on Day 1 and SOFArenal = 0 on Day 3, n = 50) and worsening AKI (SOFArenal = 0 on Days 1 and SOFArenal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p < 0.05) between the persistent and transient AKI groups, with enrichment in the plasma membrane complex, receptor complex, and T-cell receptor complex. A 43-gene SVM model was developed using the genetic algorithm, which showed significantly greater performance predicting persistent AKI than the model based on clinical variables in a holdout subset (AUC: 0.948 [0.912, 0.984] vs. 0.739 [0.648, 0.830]; p < 0.01 for Delong's test). CONCLUSIONS Our study identified four subtypes of sepsis-induced AKI based on kidney injury trajectories. The landscape of host response aberrations across these subtypes was characterized. An SVM model based on a gene signature was developed to predict renal function trajectories, and showed better performance than the clinical variable-based model. Future studies are warranted to validate the gene model in distinguishing persistent from transient AKI.
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Affiliation(s)
- Zhongheng Zhang
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 People’s Republic of China
| | - Lin Chen
- grid.13402.340000 0004 1759 700XDepartment of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People’s Republic of China
| | - Huiheng Liu
- grid.413280.c0000 0004 0604 9729Emergency Department, Zhongshan Hospital of Xiamen University, Xiamen, Fujian People’s Republic of China
| | - Yujing Sun
- grid.413280.c0000 0004 0604 9729Emergency Department, Zhongshan Hospital of Xiamen University, Xiamen, Fujian People’s Republic of China
| | - Pengfei Shui
- grid.411634.50000 0004 0632 4559Department of Emergency, People’s Hospital of Anji, Anji County, Zhejiang People’s Republic of China
| | - Jian Gao
- Department of Critical Medicine, Pi County Peoples Hospital, Chengdu, People’s Republic of China
| | - Decong Wang
- Department of Critical Medicine, Pi County Peoples Hospital, Chengdu, People’s Republic of China
| | - Huilin Jiang
- grid.412534.5Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Yanling Li
- grid.412534.5Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Kun Chen
- grid.13402.340000 0004 1759 700XDepartment of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People’s Republic of China
| | - Yucai Hong
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 People’s Republic of China
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Zhang Z, Kashyap R, Su L, Meng Q. Editorial: Clinical application of artificial intelligence in emergency and critical care medicine, volume III. Front Med (Lausanne) 2022; 9:1075023. [PMID: 36600889 PMCID: PMC9806846 DOI: 10.3389/fmed.2022.1075023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 11/04/2022] [Indexed: 12/23/2022] Open
Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China,Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Hangzhou, China,*Correspondence: Zhongheng Zhang
| | - Rahul Kashyap
- Critical Care Independent Multidisciplinary Program, Mayo Clinic, Rochester, MN, United States,Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States
| | - Longxiang Su
- State Key Laboratory of Complex Severe and Rare Diseases, Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Qinghe Meng
- Department of Surgery, State University of New York Upstate Medical University, Syracuse, NY, United States
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Abstract
PURPOSE OF REVIEW The purpose of this review is to describe acute kidney injury (AKI) phenotypes in children. RECENT FINDINGS AKI is a heterogenous disease that imposes significant morbidity and mortality on critically ill and noncritically ill patients across the age spectrum. As our understanding of AKI and its association with outcomes has improved, it is becoming increasingly apparent that there are distinct AKI subphenotypes that vary by cause or associated conditions. We have also learned that severity, duration, and repeated episodes of AKI impact outcomes, and that integration of novel urinary biomarkers of tubular injury can also reveal unique subphenotypes of AKI that may not be otherwise readily apparent. SUMMARY Studies that further delineate these unique AKI subphenotypes are needed to better understand the impact of AKI in children. Further delineation of these phenotypes has both prognostic and therapeutic implications.
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Editorial: Renal system section in current opinion in critical care: pathways forward for innovation in acute kidney injury. Curr Opin Crit Care 2022; 28:581-582. [PMID: 36302193 DOI: 10.1097/mcc.0000000000000989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Abstract
PURPOSE OF REVIEW Acute kidney injury is a heterogeneous syndrome and as such is associated with multiple predisposing conditions and causes all of which affect outcomes. Such heterogeneity may conceal the potential benefit of therapies when generally applied to patients with acute kidney injury (AKI). The discovery of pathophysiology-based subphenotypes could be of benefit in allocating current and future therapies to specific groups. RECENT FINDINGS Clinical subphenotypes group patients into categories according to predisposing factors, disease severity, and trajectory. These may be helpful in assessing patient outcomes. Analyses of existing databases have revealed biological subphenotypes that are characterized by levels of biomarkers indicative of hyperinflammation and endothelial injury. Patients with increased levels of these biomarkers display higher mortality rates compared with those with lower levels and there is potential that this group might respond differently to therapies. However, challenges remain in the validation, generalizability, and application of these subphenotypes. SUMMARY Subphenotyping may help reduce heterogeneity under the umbrella term of acute kidney injury. Despite challenges remain, the identification of AKI subphenotypes has opened the potential of AKI research focused on better targeted therapies.
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Hu C, Li Y, Wang F, Peng Z. Application of Machine Learning for Clinical Subphenotype Identification in Sepsis. Infect Dis Ther 2022; 11:1949-1964. [PMID: 36006560 DOI: 10.1007/s40121-022-00684-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/02/2022] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION Sepsis is a heterogeneous clinical syndrome. Identification of sepsis subphenotypes could lead to allowing more precise therapy. However, there is a lack of models to identify the subphenotypes in such patients. Thus, we aimed to identify possible subphenotypes and compare the clinical outcomes for subphenotypes in a large sepsis cohort. METHODS This machine learning-based, cluster analysis was performed using the Medical Information Mart in Intensive Care (MIMIC)-IV database. We enrolled all adult (> 18 years old) patients diagnosed with sepsis in the first 24 h after intensive care unit (ICU) admission. K-means cluster analysis was performed to identify the number of classes. Multivariable logistic regression models were used to estimate the association between sepsis subphenotypes and in-hospital mortality. RESULTS A total of 8817 participants with sepsis were enrolled. The median age was 66.8 (IQR, 55.9-77.1) years, and 38.1% (3361/8817) were female. Two subphenotypes resulted in optimal separation including 11 routinely available clinical variables obtained during the first 24 h after ICU admission. Participants in subphenotype B showed higher levels of lactate, glucose and creatinine, white blood cell count, sodium and heart rate and lower body temperature, platelet count, systolic blood pressure, hemoglobin and PaO2/FiO2 ratio. In addition, the in-hospital mortality in patients with subphenotype B was significantly higher than that in subphenotype A (29.4% vs. 8.5%, P < 0.001). The difference was still significant after adjustment for potential covariates (adjusted OR 2.214; 95% CI 1.780-2.754, P < 0.001). CONCLUSIONS Two sepsis subphenotypes with different clinical outcomes could be rapidly identified using the K-means clustering analysis based on routinely available clinical data. This finding may help clinicians to identify the subphenotype rapidly at the bedside.
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Affiliation(s)
- Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, 430071, Hubei, China
| | - Yiming Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, 430071, Hubei, China
| | - Fengyun Wang
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, 430071, Hubei, China
| | - Zhiyong Peng
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China. .,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, 430071, Hubei, China.
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Vaara ST, Bhatraju PK, Stanski NL, McMahon BA, Liu K, Joannidis M, Bagshaw SM. Subphenotypes in acute kidney injury: a narrative review. Crit Care 2022; 26:251. [PMID: 35986336 PMCID: PMC9389711 DOI: 10.1186/s13054-022-04121-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 07/20/2022] [Indexed: 11/26/2022] Open
Abstract
Acute kidney injury (AKI) is a frequently encountered syndrome especially among the critically ill. Current diagnosis of AKI is based on acute deterioration of kidney function, indicated by an increase in creatinine and/or reduced urine output. However, this syndromic definition encompasses a wide variety of distinct clinical features, varying pathophysiology, etiology and risk factors, and finally very different short- and long-term outcomes. Lumping all AKI together may conceal unique pathophysiologic processes specific to certain AKI populations, and discovering these AKI subphenotypes might help to develop targeted therapies tackling unique pathophysiological processes. In this review, we discuss the concept of AKI subphenotypes, current knowledge regarding both clinical and biomarker-driven subphenotypes, interplay with AKI subphenotypes and other ICU syndromes, and potential future and clinical implications.
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Affiliation(s)
- Suvi T Vaara
- Division of Intensive Care Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine, Meilahti Hospital, University of Helsinki and Helsinki University Hospital, PO Box 340, 00290, Helsinki, Finland.
| | - Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, USA
- Sepsis Center of Research Excellence (SCORE), University of Washington, Seattle, USA
| | - Natalja L Stanski
- Division of Critical Care Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, USA
| | - Blaithin A McMahon
- Division of Nephrology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Kathleen Liu
- Divisions of Nephrology and Critical Care, Departments of Medicine and Anesthesia, University of California, San Francisco, USA
| | - Michael Joannidis
- Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, Canada
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33
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Legrand M, Bagshaw SM, Koyner JL, Schulman IH, Mathis MR, Bernholz J, Coca S, Gallagher M, Gaudry S, Liu KD, Mehta RL, Pirracchio R, Ryan A, Steubl D, Stockbridge N, Erlandsson F, Turan A, Wilson FP, Zarbock A, Bokoch MP, Casey JD, Rossignol P, Harhay MO. Optimizing the Design and Analysis of Future AKI Trials. J Am Soc Nephrol 2022; 33:1459-1470. [PMID: 35831022 PMCID: PMC9342638 DOI: 10.1681/asn.2021121605] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
AKI is a complex clinical syndrome associated with an increased risk of morbidity and mortality, particularly in critically ill and perioperative patient populations. Most AKI clinical trials have been inconclusive, failing to detect clinically important treatment effects at predetermined statistical thresholds. Heterogeneity in the pathobiology, etiology, presentation, and clinical course of AKI remains a key challenge in successfully testing new approaches for AKI prevention and treatment. This article, derived from the "AKI" session of the "Kidney Disease Clinical Trialists" virtual workshop held in October 2021, reviews barriers to and strategies for improving the design and implementation of clinical trials in patients with, or at risk of, developing AKI. The novel approaches to trial design included in this review span adaptive trial designs that increase the knowledge gained from each trial participant; pragmatic trial designs that allow for the efficient enrollment of sufficiently large numbers of patients to detect small, but clinically significant, treatment effects; and platform trial designs that use one trial infrastructure to answer multiple clinical questions simultaneously. This review also covers novel approaches to clinical trial analysis, such as Bayesian analysis and assessing heterogeneity in the response to therapies among trial participants. We also propose a road map and actionable recommendations to facilitate the adoption of the reviewed approaches. We hope that the resulting road map will help guide future clinical trial planning, maximize learning from AKI trials, and reduce the risk of missing important signals of benefit (or harm) from trial interventions.
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Affiliation(s)
- Matthieu Legrand
- Department of Anesthesia and Perioperative Care, Division of Critical Care Medicine, University of California San Francisco, San Francisco, California
- French Clinical Research Infrastructure Network, Investigation Network Initiative Cardiovascular and Renal Trialists, Nancy, France
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Jay L Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Ivonne H Schulman
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Michael R Mathis
- Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan
| | | | - Steven Coca
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Martin Gallagher
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Stéphane Gaudry
- French Clinical Research Infrastructure Network, Investigation Network Initiative Cardiovascular and Renal Trialists, Nancy, France
- Département de Réanimation, Medical and surgical intensive care unit, Assistance Publique-Hôpitaux de Paris Hôpital Avicenne, Bobigny, France
- Common and Rare Kidney Diseases, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-S 1155, Paris, France
| | - Kathleen D Liu
- Divisions of Nephrology and Critical Care Medicine, Departments of Medicine and Anesthesia, University of California San Francisco, San Francisco, California
| | - Ravindra L Mehta
- Department of Medicine, University of California San Diego, San Diego, California
| | - Romain Pirracchio
- Department of Anesthesia and Perioperative Medicine, University of California San Francisco, San Francisco, California
| | - Abigail Ryan
- Division of Chronic Care Management, Chronic Care Policy Group, Center for Medicare, Center for Medicare and Medicaid Services, Baltimore, Maryland
| | - Dominik Steubl
- Boehringer Ingelheim International GmbH, Ingelheim, Germany
- Department of Nephrology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Norman Stockbridge
- Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland
| | | | - Alparslan Turan
- Department of Anesthesiology, Lerner College of Medicine of Case Western University, Cleveland, Ohio
- Department of Outcomes Research, Cleveland Clinic, Cleveland, Ohio
| | - F Perry Wilson
- Section of Nephrology and Clinical and Translational Research Accelerator, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Alexander Zarbock
- Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany
| | - Michael P Bokoch
- Department of Anesthesia and Perioperative Care, Division of Critical Care Medicine, University of California San Francisco, San Francisco, California
| | - Jonathan D Casey
- Division of Allergy, Pulmonary, and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Patrick Rossignol
- French Clinical Research Infrastructure Network, Investigation Network Initiative Cardiovascular and Renal Trialists, Nancy, France
- University of Lorraine, INSERM CIC 1433, Nancy, France
- Nancy CHRU, INSERM U1116, Nancy, French national institute of Health and Medical Research, unit 1116, Nancy, France
| | - Michael O Harhay
- Clinical Trials Methods and Outcomes Laboratory, PAIR (Palliative and Advanced Illness Research) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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Soussi S, Sharma D, Jüni P, Lebovic G, Brochard L, Marshall JC, Lawler PR, Herridge M, Ferguson N, Del Sorbo L, Feliot E, Mebazaa A, Acton E, Kennedy JN, Xu W, Gayat E, Dos Santos CC. Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort. Crit Care 2022; 26:114. [PMID: 35449071 PMCID: PMC9022336 DOI: 10.1186/s13054-022-03972-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 03/27/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Late mortality risk in sepsis-survivors persists for years with high readmission rates and low quality of life. The present study seeks to link the clinical sepsis-survivors heterogeneity with distinct biological profiles at ICU discharge and late adverse events using an unsupervised analysis. METHODS In the original FROG-ICU prospective, observational, multicenter study, intensive care unit (ICU) patients with sepsis on admission (Sepsis-3) were identified (N = 655). Among them, 467 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct sepsis-survivors clinical classes using readily available data at ICU discharge. The primary endpoint was one-year mortality after ICU discharge. RESULTS At ICU discharge, two distinct subtypes were identified (A and B) using 15 readily available clinical and biological variables. Patients assigned to subtype B (48% of the studied population) had more impaired cardiovascular and kidney functions, hematological disorders and inflammation at ICU discharge than subtype A. Sepsis-survivors in subtype B had significantly higher one-year mortality compared to subtype A (respectively, 34% vs 16%, p < 0.001). When adjusted for standard long-term risk factors (e.g., age, comorbidities, severity of illness, renal function and duration of ICU stay), subtype B was independently associated with increased one-year mortality (adjusted hazard ratio (HR) = 1.74 (95% CI 1.16-2.60); p = 0.006). CONCLUSIONS A subtype with sustained organ failure and inflammation at ICU discharge can be identified from routine clinical and laboratory data and is independently associated with poor long-term outcome in sepsis-survivors. Trial registration NCT01367093; https://clinicaltrials.gov/ct2/show/NCT01367093 .
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Affiliation(s)
- Sabri Soussi
- Interdepartmental Division of Critical Care, Faculty of Medicine, St Michael's Hospital, Keenan Research Centre for Biomedical Science and Institute of Medical Sciences, University of Toronto, 209 Victoria St 7th Floor, Toronto, ON, M5B 1T8, Canada.
| | - Divya Sharma
- Department of Biostatistics, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - Peter Jüni
- Applied Health Research Centre, Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, ON, M5B 1W8, Canada.,Department of Medicine and Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Gerald Lebovic
- Applied Health Research Centre, Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, ON, M5B 1W8, Canada.,Department of Medicine and Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Laurent Brochard
- Interdepartmental Division of Critical Care, Faculty of Medicine, St Michael's Hospital, Keenan Research Centre for Biomedical Science and Institute of Medical Sciences, University of Toronto, 209 Victoria St 7th Floor, Toronto, ON, M5B 1T8, Canada
| | - John C Marshall
- Interdepartmental Division of Critical Care, Faculty of Medicine, St Michael's Hospital, Keenan Research Centre for Biomedical Science and Institute of Medical Sciences, University of Toronto, 209 Victoria St 7th Floor, Toronto, ON, M5B 1T8, Canada
| | - Patrick R Lawler
- Peter Munk Cardiac Centre, University Health Network, and Heart and Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, University of Toronto, Toronto, ON, Canada
| | - Margaret Herridge
- Department of Medicine, Interdepartmental Division of Critical Care Medicine, Toronto General Research Institute, Institute of Medical Science, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Niall Ferguson
- Department of Medicine, Interdepartmental Division of Critical Care Medicine, Toronto General Research Institute, Institute of Medical Science, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Lorenzo Del Sorbo
- Department of Medicine, Interdepartmental Division of Critical Care Medicine, Toronto General Research Institute, Institute of Medical Science, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Elodie Feliot
- Department of Anesthesiology, Critical Care, Lariboisière - Saint-Louis Hospitals, DMU Parabol, AP-HP Nord; Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), University of Paris, Paris, France
| | - Alexandre Mebazaa
- Department of Anesthesiology, Critical Care, Lariboisière - Saint-Louis Hospitals, DMU Parabol, AP-HP Nord; Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), University of Paris, Paris, France
| | - Erica Acton
- Interdepartmental Division of Critical Care, Faculty of Medicine, St Michael's Hospital, Keenan Research Centre for Biomedical Science and Institute of Medical Sciences, University of Toronto, 209 Victoria St 7th Floor, Toronto, ON, M5B 1T8, Canada
| | - Jason N Kennedy
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - Etienne Gayat
- Department of Anesthesiology, Critical Care, Lariboisière - Saint-Louis Hospitals, DMU Parabol, AP-HP Nord; Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), University of Paris, Paris, France
| | - Claudia C Dos Santos
- Interdepartmental Division of Critical Care, Faculty of Medicine, St Michael's Hospital, Keenan Research Centre for Biomedical Science and Institute of Medical Sciences, University of Toronto, 209 Victoria St 7th Floor, Toronto, ON, M5B 1T8, Canada
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Legouis D, Criton G, Assouline B, Le Terrier C, Sgardello S, Pugin J, Marchi E, Sangla F. Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients. Front Med (Lausanne) 2022; 9:980160. [PMID: 36275817 PMCID: PMC9579431 DOI: 10.3389/fmed.2022.980160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors. Methods We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals. Results Among the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality. Conclusion We propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology.
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Affiliation(s)
- David Legouis
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital of Geneva, Geneva, Switzerland
- *Correspondence: David Legouis
| | - Gilles Criton
- Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland
| | - Benjamin Assouline
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Christophe Le Terrier
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Sebastian Sgardello
- Department of Surgery, Center Hospitalier du Valais Romand, Sion, Switzerland
| | - Jérôme Pugin
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Elisa Marchi
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Frédéric Sangla
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland
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36
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Ozrazgat-Baslanti T, Loftus TJ, Ren Y, Ruppert MM, Bihorac A. Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury. Curr Opin Crit Care 2021; 27:560-572. [PMID: 34757993 PMCID: PMC8783984 DOI: 10.1097/mcc.0000000000000887] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients. RECENT FINDINGS Among recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies. SUMMARY Use of consensus criteria, standard definitions and common data models could facilitate access to machine learning-ready data sets for external validation. The lack of interpretability, explainability, fairness and transparency of artificial intelligence models hinder their entrustment and clinical implementation; compliance with standardized reporting guidelines can mitigate these challenges.
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Affiliation(s)
- Tezcan Ozrazgat-Baslanti
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Tyler J. Loftus
- Department of Surgery, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Yuanfang Ren
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Matthew M. Ruppert
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Azra Bihorac
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
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Timing of renal-replacement therapy in intensive care unit-related acute kidney injury. Curr Opin Crit Care 2021; 27:573-581. [PMID: 34757994 DOI: 10.1097/mcc.0000000000000891] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
PURPOSE OF REVIEW The optimal timing of renal-replacement therapy (RRT) initiation for the management of acute kidney injury (AKI) in the intensive care unit (ICU) is frequently controversial. An earlier-strategy has biological rationale, even in the absence of urgent indications; however, a delayed-strategy may prevent selected patients from receiving RRT and avoid complications related to RRT. RECENT FINDINGS Previous studies assessing the optimal timing of RRT initiation found conflicting results, contributing to variation in clinical practice. The recent multinational trial, standard vs. accelerated initiation of renal replacement therapy in acute kidney injury (STARRT-AKI) found no survival benefit and a higher risk of RRT dependence with an accelerated compared to a standard RRT initiation strategy in critically ill patients with severe AKI. Nearly 40% of patients allocated to the standard-strategy group did not receive RRT. The Artificial Kidney Initiation in Kidney Injury-2 (AKIKI-2) trial further assessed delayed compared to more-delayed strategies for RRT initiation. The more-delayed strategy did not confer an increase in RRT-free days and was associated with a higher risk of death. SUMMARY Early preemptive initiation of RRT in critically ill patients with AKI does not confer clear clinical benefits. However, protracted delays in RRT initiation may be harmful.
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Zhou B, Lin LY, Liu XA, Ling YS, Zhang YY, Luo AQ, Wu MC, Guo RM, Chen HL, Guo Q. Invasive Blood Pressure Measurement and In-hospital Mortality in Critically Ill Patients With Hypertension. Front Cardiovasc Med 2021; 8:720605. [PMID: 34540920 PMCID: PMC8440864 DOI: 10.3389/fcvm.2021.720605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/16/2021] [Indexed: 11/26/2022] Open
Abstract
Background: Invasive blood pressure (IBP) measurement is common in the intensive care unit, although its association with in-hospital mortality in critically ill patients with hypertension is poorly understood. Methods and Results: A total of 11,732 critically ill patients with hypertension from the eICU-Collaborative Research Database (eICU-CRD) were enrolled. Patients were divided into 2 groups according to whether they received IBP. The primary outcome in this study was in-hospital mortality. Propensity score matching (PSM) and inverse probability of treatment weighing (IPTW) models were used to balance the confounding covariates. Multivariable logistic regression was used to evaluate the association between IBP measurement and hospital mortality. The IBP group had a higher in-hospital mortality rate than the no IBP group in the primary cohort [238 (8.7%) vs. 581 (6.5%), p < 0.001]. In the PSM cohort, the IBP group had a lower in-hospital mortality rate than the no IBP group [187 (8.0%) vs. 241 (10.3%), p = 0.006]. IBP measurement was associated with lower in-hospital mortality in the PSM cohort (odds ratio, 0.73, 95% confidence interval, 0.59–0.92) and in the IPTW cohort (odds ratio, 0.81, 95% confidence interval, 0.67–0.99). Sensitivity analyses showed similar results in the subgroups with high body mass index and no sepsis. Conclusions: In conclusion, IBP measurement was associated with lower in-hospital mortality in critically ill patients with hypertension, highlighting the importance of IBP measurement in the intensive care unit.
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Affiliation(s)
- Bin Zhou
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Liang-Ying Lin
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiao-Ai Liu
- Institute of Nursing, Guangdong Food and Drug Vocational College, Guangzhou, China
| | - Ye-Sheng Ling
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yuan-Yuan Zhang
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - An-Qi Luo
- Department of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng-Chun Wu
- Department of Emergency Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ruo-Mi Guo
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hua-Li Chen
- Department of Nosocomial Infection Control, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qi Guo
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
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39
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Heijnen NFL, Hagens LA, Smit MR, Cremer OL, Ong DSY, van der Poll T, van Vught LA, Scicluna BP, Schnabel RM, van der Horst ICC, Schultz MJ, Bergmans DCJJ, Bos LDJ. Biological Subphenotypes of Acute Respiratory Distress Syndrome Show Prognostic Enrichment in Mechanically Ventilated Patients without Acute Respiratory Distress Syndrome. Am J Respir Crit Care Med 2021; 203:1503-1511. [PMID: 33465019 DOI: 10.1164/rccm.202006-2522oc] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Rationale: Recent studies showed that biological subphenotypes in acute respiratory distress syndrome (ARDS) provide prognostic enrichment and show potential for predictive enrichment. Objectives: To determine whether these subphenotypes and their prognostic and potential for predictive enrichment could be extended to other patients in the ICU, irrespective of fulfilling the definition of ARDS. Methods: This is a secondary analysis of a prospective observational study of adult patients admitted to the ICU. We tested the prognostic enrichment of both cluster-derived and latent-class analysis (LCA)-derived biological ARDS subphenotypes by evaluating the association with clinical outcome (ICU-day, 30-day mortality, and ventilator-free days) using logistic regression and Cox regression analysis. We performed a principal component analysis to compare blood leukocyte gene expression profiles between subphenotypes and the presence of ARDS. Measurements and Main Results: We included 2,499 mechanically ventilated patients (674 with and 1,825 without ARDS). The cluster-derived "reactive" subphenotype was, independently of ARDS, significantly associated with a higher probability of ICU mortality, higher 30-day mortality, and a lower probability of successful extubation while alive compared with the "uninflamed" subphenotype. The blood leukocyte gene expression profiles of individual subphenotypes were similar for patients with and without ARDS. LCA-derived subphenotypes also showed similar profiles. Conclusions: The prognostic and potential for predictive enrichment of biological ARDS subphenotypes may be extended to mechanically ventilated critically ill patients without ARDS. Using the concept of biological subphenotypes for splitting cohorts of critically ill patients could add to improving future precision-based trial strategies and lead to identifying treatable traits for all critically ill patients.
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Affiliation(s)
- Nanon F L Heijnen
- Department of Intensive Care Medicine, Maastricht University Medical Center+, Maastricht, the Netherlands
| | | | | | | | - David S Y Ong
- Division of Infectious Diseases.,Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Infection and Immunity
| | - Tom van der Poll
- Laboratory of Experimental Intensive Care and Anesthesiology, and.,Department of Respiratory Medicine, Amsterdam University Medical Centers, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Brendon P Scicluna
- Laboratory of Experimental Intensive Care and Anesthesiology, and.,Department of Intensive Care Medicine and
| | - Ronny M Schnabel
- Department of Intensive Care Medicine, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care Medicine, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Marcus J Schultz
- Department of Intensive Care Medicine.,Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Medical Microbiology and Infection Control, Franciscus Gasthuis and Vlietland, Rotterdam, the Netherlands.,Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand; and
| | - Dennis C J J Bergmans
- Department of Intensive Care Medicine, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Lieuwe D J Bos
- Department of Intensive Care Medicine.,Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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Acute kidney injury in the critically ill: an updated review on pathophysiology and management. Intensive Care Med 2021; 47:835-850. [PMID: 34213593 PMCID: PMC8249842 DOI: 10.1007/s00134-021-06454-7] [Citation(s) in RCA: 145] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/04/2021] [Indexed: 01/10/2023]
Abstract
Acute kidney injury (AKI) is now recognized as a heterogeneous syndrome that not only affects acute morbidity and mortality, but also a patient’s long-term prognosis. In this narrative review, an update on various aspects of AKI in critically ill patients will be provided. Focus will be on prediction and early detection of AKI (e.g., the role of biomarkers to identify high-risk patients and the use of machine learning to predict AKI), aspects of pathophysiology and progress in the recognition of different phenotypes of AKI, as well as an update on nephrotoxicity and organ cross-talk. In addition, prevention of AKI (focusing on fluid management, kidney perfusion pressure, and the choice of vasopressor) and supportive treatment of AKI is discussed. Finally, post-AKI risk of long-term sequelae including incident or progression of chronic kidney disease, cardiovascular events and mortality, will be addressed.
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41
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Wu Y, Hu H, Cai J, Chen R, Zuo X, Cheng H, Yan D. Applying latent class analysis to risk stratification of incident diabetes among Chinese adults. Diabetes Res Clin Pract 2021; 174:108742. [PMID: 33722702 DOI: 10.1016/j.diabres.2021.108742] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/22/2021] [Accepted: 03/01/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To use latent class analysis to identify unobservable subpopulations amongst the heterogeneous population and explore the relationship between subpopulations and incident diabetes among Chinese adults. METHODS The retrospective study included 32,312 Chinese adults without diabetes at baseline. Latent class indicators included demographic and clinical variables. The outcome was incident diabetes. The relationship between latent class and outcome was evaluated with Cox proportional hazard regression analysis. RESULTS After screening, the two-class latent class model best fits the population. Participants in class 2 are characterized by higher age, body mass index, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, triglyceride, low-density lipoprotein cholesterol, serum creatinine, serum urea nitrogen, alanine aminotransferase, and a higher proportion of males, ever/current smokers and drinkers, but lower high-density lipoprotein cholesterol and a lower proportion of family history of diabetes. The risk of diabetes in class 2 was 5.451 times (HR: 6.451, 95%CI: 4.179-9.960, P < 0.00001) and 5.264 times (HR: 6.264, 95%CI: 4.680-8.385, P < 0.00001) higher than that in class 1 during 3-year and 5-year follow-up, respectively. CONCLUSIONS We used latent class analysis to identify two distinct subpopulations with differential risk of diabetes during 3-year and 5-year follow-up.
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Affiliation(s)
- Yang Wu
- Department of Endocrinology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, Guangdong Province, China; Department of Endocrinology, Shenzhen Second People's Hospital, Shenzhen 518035, Guangdong Province, China; Shenzhen University Health Science Center, Shenzhen 518071, Guangdong Province, China
| | - Haofei Hu
- Department of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, Guangdong Province, China; Department of Nephrology, Shenzhen Second People's Hospital, Shenzhen 518035, Guangdong Province, China; Shenzhen University Health Science Center, Shenzhen 518071, Guangdong Province, China
| | - Jinlin Cai
- Department of Endocrinology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, Guangdong Province, China; Department of Endocrinology, Shenzhen Second People's Hospital, Shenzhen 518035, Guangdong Province, China; Shantou University Medical College, Shantou 515000, Guangdong Province, China
| | - Runtian Chen
- Department of Endocrinology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, Guangdong Province, China; Department of Endocrinology, Shenzhen Second People's Hospital, Shenzhen 518035, Guangdong Province, China; Shenzhen University Health Science Center, Shenzhen 518071, Guangdong Province, China
| | - Xin Zuo
- Department of Endocrinology, The Third People's Hospital of Shenzhen, Shenzhen 518116, Guangdong Province, China
| | - Heng Cheng
- Department of Endocrinology, The Third People's Hospital of Shenzhen, Shenzhen 518116, Guangdong Province, China
| | - Dewen Yan
- Department of Endocrinology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, Guangdong Province, China; Department of Endocrinology, Shenzhen Second People's Hospital, Shenzhen 518035, Guangdong Province, China; Shenzhen University Health Science Center, Shenzhen 518071, Guangdong Province, China.
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42
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Ronco C, Reis T. Continuous renal replacement therapy and extended indications. Semin Dial 2021; 34:550-560. [PMID: 33711166 DOI: 10.1111/sdi.12963] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/02/2021] [Accepted: 02/08/2021] [Indexed: 01/16/2023]
Abstract
Extracorporeal blood purification (EBP) techniques provide support for critically ill patients with single or multiple organ dysfunction. Continuous renal replacement therapy (CRRT) is the modality of choice for kidney support for those patients and orchestrates the interactions between the different artificial organ support systems. Intensive care teams should be familiar with the concept of sequential extracorporeal therapy and plan on how to incorporate new treatment modalities into their daily practices. Importantly, scientific evidence should guide the decision-making process at the bedside and provide robust arguments to justify the costs of implementing new EBP treatments. In this narrative review, we explore the extended indications for CRRT as an adjunctive treatment to provide support for the heart, lung, liver, and immune system. We detail practicalities on how to run the treatments and how to tackle the most frequent complications regarding each of the therapies, whether applied alone or integrated. The physicochemical processes and technologies involved at the molecular level encompassing the interactions between the molecules, membranes, and resins are spotlighted. A clinical case will illustrate the timing for the initiation, maintenance, and discontinuation of EBP techniques.
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Affiliation(s)
- Claudio Ronco
- Department of Medicine (DIMED), University of Padova, Padova, Italy.,Department of Nephrology, Dialysis and Transplantation, International Renal Research Institute of Vicenza (IRRIV), San Bortolo Hospital, Vicenza, Italy.,National Academy of Medicine, Young Leadership Physicians Program, Rio de Janeiro, Brazil
| | - Thiago Reis
- Department of Nephrology, Dialysis and Transplantation, International Renal Research Institute of Vicenza (IRRIV), San Bortolo Hospital, Vicenza, Italy.,Department of Nephrology, Clínica de Doenças Renais de Brasília, Molecular Pharmacology Laboratory, University of Brasília, Brasilia, Brazil
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43
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Sinha P, Calfee CS, Delucchi KL. Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. Crit Care Med 2021; 49:e63-e79. [PMID: 33165028 PMCID: PMC7746621 DOI: 10.1097/ccm.0000000000004710] [Citation(s) in RCA: 185] [Impact Index Per Article: 61.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overview of the principles behind latent class analysis. Furthermore, in a stepwise manner, we outline the key processes necessary to perform latent class analysis including some of the challenges and pitfalls faced at each of these steps. The review provides a one-stop shop for investigators seeking to apply latent class analysis to their data.
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Affiliation(s)
- Pratik Sinha
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine; University of California, San Francisco; San Francisco, CA
- Department of Anesthesia; University of California, San Francisco; San Francisco, CA
| | - Carolyn S. Calfee
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine; University of California, San Francisco; San Francisco, CA
- Department of Anesthesia; University of California, San Francisco; San Francisco, CA
| | - Kevin L. Delucchi
- Department of Psychiatry; University of California, San Francisco; San Francisco, CA
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44
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Odum JD, Wong HR, Stanski NL. A Precision Medicine Approach to Biomarker Utilization in Pediatric Sepsis-Associated Acute Kidney Injury. Front Pediatr 2021; 9:632248. [PMID: 33937146 PMCID: PMC8079650 DOI: 10.3389/fped.2021.632248] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 03/22/2021] [Indexed: 12/29/2022] Open
Abstract
Sepsis is a leading cause of morbidity and mortality in critically ill children, and acute kidney injury (AKI) is a frequent complication that confers an increased risk for poor outcomes. Despite the documented consequences of sepsis-associated AKI (SA-AKI), no effective disease-modifying therapies have been identified to date. As such, the only treatment options for these patients remain prevention and supportive care, both of which rely on the ability to promptly and accurately identify at risk and affected individuals. To achieve these goals, a variety of biomarkers have been investigated to help augment our currently limited predictive and diagnostic strategies for SA-AKI, however, these have had variable success in pediatric sepsis. In this mini-review, we will briefly outline the current use of biomarkers for SA-AKI, and propose a new framework for biomarker discovery and utilization that considers the individual patient's sepsis inflammatory response. Now recognized to be a key driver in the complex pathophysiology of SA-AKI, understanding the dysregulated host immune response to sepsis is a growing area of research that can and should be leveraged to improve the prediction and diagnosis of SA-AKI, while also potentially identifying novel therapeutic targets. Reframing SA-AKI in this manner - as a direct consequence of the individual patient's sepsis inflammatory response - will facilitate a precision medicine approach to its management, something that is required to move the care of this consequential disorder forward.
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Affiliation(s)
- James D Odum
- Division of Critical Care, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hector R Wong
- Division of Critical Care, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Natalja L Stanski
- Division of Critical Care, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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45
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Basu RK, Hackbarth R, Gillespie S, Akcan-Arikan A, Brophy P, Bagshaw S, Alobaidi R, Goldstein SL. Clinical phenotypes of acute kidney injury are associated with unique outcomes in critically ill septic children. Pediatr Res 2021; 90:1031-1038. [PMID: 33531676 PMCID: PMC7852056 DOI: 10.1038/s41390-021-01363-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/04/2020] [Accepted: 12/25/2020] [Indexed: 01/31/2023]
Abstract
BACKGROUND Assessment of acute kidney injury (AKI) in septic patients remains imprecise. In adults, the classification of septic patients by clinical AKI phenotypes (severity and timing) demonstrates unique associations with patient outcome vs. broadly defined AKI. METHODS In a multinational prospective observational study, AKI diagnosis in critically ill septic children was stratified by duration (transient vs. persistent) and severity (mild vs. severe by creatinine change and urine output). The outcomes of interest were mortality and intensive care unit resource complexity at 28 days. RESULTS Seven hundred and fifty-seven septic children were studied (male 52.7%, age 4.6 years (1.5-11.9)). Mortality (overall 12.1%) was different between severe AKI and mild AKI (18.3 vs. 4.4%, p < 0.001) as well as intensive care unit (ICU) complexity (overall 34.5%, 45 vs. 21.7%, p < 0.001). Patients with Persistent AKI had fewer ICU-free days (17 (7, 21) vs. 24 (17, 26), p < 0.001) and higher ICU complexity (52.8 vs. 22.9%, p = 0.002) than transient AKI, even after exclusion of patients with early mortality. AKI phenotypes incorporating temporal and severity data correlate with unique survival (range 4.4-21.6%) and ICU-free days (range of 15-25 days) CONCLUSIONS: The outcome of septic children with AKI changes by clinical phenotype. Our findings underscore the importance of prognostic enrichment in sepsis and AKI for the purpose of trial design and patient management. IMPACT Although AKI occurs commonly in patients with sepsis (S-AKI), outcomes for children with S-AKI varies based on the severity and timing of the AKI. Existing S-AKI pediatric data utilize a broad singular definition of kidney injury. Increasing the precision of AKI classification results in a new understanding of how S-AKI associates with patient outcome. A refined classification of S-AKI identifies subgroups of children, making possible a targeted and a personalized medicine approach to S-AKI study and management.
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Affiliation(s)
- Rajit K. Basu
- grid.189967.80000 0001 0941 6502Division of Pediatric Critical Care, Children’s Healthcare of Atlanta, Emory University, Atlanta, GA USA
| | - Richard Hackbarth
- grid.416230.20000 0004 0406 3236Division of Pediatric Critical Care, Helen DeVos Children’s Hospital, Spectrum Health, Grand Rapids, MI USA
| | - Scott Gillespie
- grid.189967.80000 0001 0941 6502Department of Pediatrics, Division of Biostatistics, Emory University, Atlanta, GA USA
| | - Ayse Akcan-Arikan
- grid.416975.80000 0001 2200 2638Department of Pediatrics, Sections of Critical Care and Nephrology, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX USA
| | - Patrick Brophy
- grid.438870.00000 0004 0451 2572Department of Pediatrics, Golisano Children’s Hospital, Fort Myers, FL USA
| | - Sean Bagshaw
- grid.17089.37Stollery Children’s Hospital, University of Alberta, Edmonton, AB Canada
| | - Rashid Alobaidi
- grid.17089.37Department of Pediatrics, University of Alberta, Edmonton, AB Canada
| | - Stuart L. Goldstein
- grid.239573.90000 0000 9025 8099Center for Acute Care Nephrology, Cincinnati Children’s Hospital, Cincinnati, OH USA
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Ostermann M. Editorial: Management of acute kidney injury during critical illness - what is on the horizon? Curr Opin Crit Care 2020; 26:517-518. [PMID: 33109948 DOI: 10.1097/mcc.0000000000000780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Marlies Ostermann
- Department of Critical Care and Nephrology, King's College London, Guy's & St. Thomas' Hospital, London SE1 7EH, UK
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Abstract
PURPOSE OF REVIEW AKI is a complex clinical syndrome with many causes and there is a broad range of clinical presentations that vary according to duration, severity and context. Established consensus definitions of AKI are nonspecific and limited to kidney function. This reduces treatment options to generic approaches rather than individualized, cause-based strategies that have limited both understanding and management of AKI. RECENT FINDINGS The context and the temporal phase of kidney injury are critical features in the course of AKI and critical to timing-relevant intervention. These features are missing in generic definitions and terms used to describe AKI. Subphenotypes of AKI can be identified from novel damage biomarkers, from functional changes including creatinine trajectories, from the duration of change and from associated clinical characteristics and comorbidities. Subphenotype parameters can be combined in risk scores, or by association strategies ranging from a simple function-damage matrix to complex methods, such as machine learning. Examples of such strategies are reviewed along with tentative proposals for a revised nomenclature to facilitate description of AKI subphenotypes. SUMMARY Appropriate intervention requires refinement of the nomenclature of AKI to identify subphenotypes that facilitate correctly timed and selectively targeted intervention.
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Affiliation(s)
- Zoltan H Endre
- Department of Nephrology, Prince of Wales Hospital and Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Ravindra L Mehta
- Division of Nephrology, Department of Medicine, University of California San Diego, San Diego, California, USA
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48
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Zhang Z, Pan Q, Ge H, Xing L, Hong Y, Chen P. Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values. EBioMedicine 2020; 62:103081. [PMID: 33181462 PMCID: PMC7658497 DOI: 10.1016/j.ebiom.2020.103081] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 09/19/2020] [Accepted: 10/07/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets. METHODS The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance. FINDINGS A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 - 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 - 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 - 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211). INTERPRETATION Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership. FUNDING The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).
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Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
| | - Qing Pan
- College of Information Engineering, Zhejiang University of Technology, 310023, Hangzhou, China.
| | - Huiqing Ge
- Department of Respiratory Care, Sir Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Lifeng Xing
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
| | - Yucai Hong
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Pengpeng Chen
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
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Thau MR, Bhatraju PK. Sub-Phenotypes of Acute Kidney Injury: Do We Have Progress for Personalizing Care? Nephron Clin Pract 2020; 144:677-679. [PMID: 33091901 PMCID: PMC7708595 DOI: 10.1159/000511321] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 09/02/2020] [Indexed: 11/19/2022] Open
Abstract
Acute kidney injury (AKI) is the most common form of organ dysfunction occurring in patients admitted to the intensive care unit and contributes significantly to poor long-term outcomes. Despite this public health impact, no effective pharmacotherapy exists for AKI. One reason may be that heterogeneity is present within AKI as currently defined, thereby concealing unique pathophysiologic processes specific to certain AKI populations. Supporting this notion, we and others have shown that diversity within the AKI clinical syndrome exists, and the "one-size-fits-all" approach by current diagnostic guidelines may not be ideal. A "precision medicine" approach that exploits an individual's genetic, biologic, and clinical characteristics to identify AKI sub-phenotypes may overcome such limitations. Identification of AKI sub-phenotypes may address a critical unmet clinical need in AKI by (1) improving risk prognostication, (2) identifying novel pathophysiology, and (3) informing a patient's likelihood of responding to current therapeutics or establishing new therapeutic targets to prevent and treat AKI. This review discusses the current state of phenotyping AKI and future directions.
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Affiliation(s)
- Matthew R Thau
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington, USA,
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington, USA,
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Zhang Z, Chen L, Xu P, Xing L, Hong Y, Chen P. Gene correlation network analysis to identify regulatory factors in sepsis. J Transl Med 2020; 18:381. [PMID: 33032623 PMCID: PMC7545567 DOI: 10.1186/s12967-020-02561-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 10/03/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. METHODS The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA. RESULTS A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018). CONCLUSIONS The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.
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Affiliation(s)
- Zhongheng Zhang
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016 Zhejiang Province China
| | - Lin Chen
- grid.13402.340000 0004 1759 700XDepartment of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Ping Xu
- Emergency Department, Zigong Fourth People’s Hospital, 19 Tanmulin Road, Zigong, Sichuan China
| | - Lifeng Xing
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016 Zhejiang Province China
| | - Yucai Hong
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016 Zhejiang Province China
| | - Pengpeng Chen
- grid.13402.340000 0004 1759 700XDepartment of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016 Zhejiang Province China
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