Evidence map›Paper›PMID 23899564›Full record

ArticlePhysiological reviews2013

Sepsis: multiple abnormalities, heterogeneous responses, and evolving understanding.

Kendra N Iskander, Marcin F Osuchowski, Deborah J Stearns-Kurosawa, Shinichiro Kurosawa, David Stepien, Catherine Valentine, Daniel G Remick

4 registry-linked trialsAbstract read
In one paragraph

Article in Physiological reviews, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Cited by 233 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
233citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03977688 completednot on this mapstarted 2019, after this paper: background citation

Evaluating a CytoSorb Score in Septic Shock - a Retrospective Multicenter Data Analysis

TypeobservationalSponsorKlinikum EmdenRan2019 to 2019Enrolled500ConditionsSevere Sepsis With Septic ShockArmsCytokin Adsorption
NCT04742764 phase3suspendednot on this mapstarted 2024, after this paper: background citation

Dosing of Extracorporeal Cytokine Removal In Septic Shock (DECRISS): a Prospective, Randomized, Multicenter Clinical Trial

TypeinterventionalSponsorUniversity of PecsRan2024 to 2027Enrolled135ConditionsSeptic ShockArmsStandard medical therapy, Standard medical therapy plus cytokine removal treatment using Cytosorb, with the adsorber changed in every 12 hours, Standard medical therapy plus cytokine removal treatment using Cytosorb, with the adsorber changed in every 24 hours
NCT05042622 naunknown statusnot on this mapstarted 2021, after this paper: background citation

Innovative Solutions to Organ-substituting Technologies in the Treatment of Heart and Pulmonary Failure

TypeinterventionalSponsorNational Research Center for Cardiac Surgery, KazakhstanRan2021 to 2023Enrolled90ConditionsAcute Respiratory Distress Syndrome, Septic Shock, SepsisArmsCytokine adsorber filter, Extracorporeal hemoperfusion cartridge
NCT05090930 naunknown statusnot on this mapstarted 2021, after this paper: background citation

Development of Innovative Technologies for the Treatment of Pulmonary and Heart Failure to Prolong Human's Life

TypeinterventionalSponsorNational Research Center for Cardiac Surgery, KazakhstanRan2021 to 2023Enrolled100ConditionsCardiogenic Shock, Ventricular Arrythmia, Cardiac Arrest With Successful Resuscitation, SepsisArmsHA330 - Jafron Biomedical Co., Ltd., China - extracorporeal hemocorrection, CytoSorb (CytoSorbents Corp., USA) - an extracorporeal cytokine adsorber
3 · Its place in the literature

Who cites it

233 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. MicroRNAs and Sepsis-Induced Cardiac Dysfunction: A Systematic Review.International journal of molecular sciences · 2020
    Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Trial
  9. Trial
  10. Article
  11. Observational
  12. Article
  13. Review
  14. Review
  15. Review
  16. Diagnostic accuracy of PCT, IL-6, and MR-ProADM for early identification of sepsis in the emergency department.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2026
    Observational
  17. Article
  18. Review
  19. Review
  20. Article

173 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kendra N IskanderDepartment of Pathology, Boston University School of Medicine, Boston, Massachusetts, USA.
Marcin F Osuchowski
Deborah J Stearns-Kurosawa
Shinichiro Kurosawa
David Stepien
Catherine Valentine
Daniel G Remick

Funding

Translation of immunologic technologies from basic research into pre-clinical nonU19AI062629 · NIAID · OKLAHOMA MEDICAL RESEARCH FOUNDATION · PI COGGESHALL, KENNETH MARK · 2004 to 2023
$56.2M
CORE--BABOON RESEARCH RESOURCE PROGRAMP40RR012317 · NCRR · UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR · PI WHITE, GARY L · 1998 to 2011
$13.0M
SHIGA-TOXINS: PRE-CLINICAL ANIMAL MODEL DEVELOPMENT AND THERAPEUTIC TESTINGU01AI075386 · NIAID · OKLAHOMA MEDICAL RESEARCH FOUNDATION · PI KUROSAWA, SHINICHIRO · 2007 to 2011
$3.7M
Impact of Biological, Clinical, and Social Determinants on Trauma and Trauma OutcomesT32GM086308 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Alik Farber, Daniel G. Remick · 2010 to 2026
$3.5M
PRIMATE MODEL AND PATHOGENESIS OF ANTHRAX SEPSISR01AI058107 · NIAID · OKLAHOMA MEDICAL RESEARCH FOUNDATION · PI KUROSAWA, SHINICHIRO · 2005 to 2009
$2.4M
Role of Cytokines in Sepsis and TraumaR01GM082962 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI REMICK, DANIEL G. · 2009 to 2012
$1.3M
Adenosine and Oxygen Modulate Antimicrobial DefensesR01GM097320 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI REMICK, DANIEL G., SITKOVSKY, MICHAIL · 2011 to 2014
$1.2M
Adenosine and TLR regulation of costimulatory molecule expression in sepsisK08GM100190 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI VALENTINE, CATHERINE · 2012 to 2016
$810k
NCRR NIH HHS P40 RR012317NIAID NIH HHS R01 AI058107NIAID NIH HHS U01 AI075386NIAID NIH HHS U19 AI062629NIGMS NIH HHS K08 GM100190NIGMS NIH HHS R01 GM097320NIGMS NIH HHS R01 GM82962NIGMS NIH HHS R01 GM97320NIGMS NIH HHS T32 GM086308NIGMS NIH HHS T32 GM86308
6 · The paper itself

Abstract

Sepsis represents the host's systemic inflammatory response to a severe infection. It causes substantial human morbidity resulting in hundreds of thousands of deaths each year. Despite decades of intense research, the basic mechanisms still remain elusive. In either experimental animal models of sepsis or human patients, there are substantial physiological changes, many of which may result in subsequent organ injury. Variations in age, gender, and medical comorbidities including diabetes and renal failure create additional complexity that influence the outcomes in septic patients. Specific system-based alterations, such as the coagulopathy observed in sepsis, offer both potential insight and possible therapeutic targets. Intracellular stress induces changes in the endoplasmic reticulum yielding misfolded proteins that contribute to the underlying pathophysiological changes. With these multiple changes it is difficult to precisely classify an individual's response in sepsis as proinflammatory or immunosuppressed. This heterogeneity also may explain why most therapeutic interventions have not improved survival. Given the complexity of sepsis, biomarkers and mathematical models offer potential guidance once they have been carefully validated. This review discusses each of these important factors to provide a framework for understanding the complex and current challenges of managing the septic patient. Clinical trial failures and the therapeutic interventions that have proven successful are also discussed.

Indexed as

AgingAnimalsHumansSepsisSex FactorsStress, Physiological

Identifiers

PMID23899564
PMCPMC3962548

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.