ReviewMedizinische Klinik, Intensivmedizin und Notfallmedizin2023
Corticosteroids in sepsis and community-acquired pneumonia.
Review in Medizinische Klinik, Intensivmedizin und Notfallmedizin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- CSA-13 treatment in a murine model of Pseudomonas pneumonia-induced sepsis and its effects on renal injury, inflammation, and glycocalyx derangement.The Journal of antibiotics · 2026Article
- Early diagnostic value of Serum GDF-15, RBP4 and SOCS3 in severe pneumonia with sepsis.Journal of medical biochemistry · 2026Article
- Prognostic value of multivariate logistic regression analysis and amyloid A lactate monitoring in patients with severe pneumonia-associated sepsis.BMC pulmonary medicine · 2025Article
- A narrative review of the pathophysiology of sepsis in sub-Saharan Africa: Exploring the potential for corticosteroid therapy.PLOS global public health · 2025Review
- Plasma Exosomal miR-17-5p Regulates Macrophage Polarization by Targeting Bcl11b in Sepsis-Induced Lung Injury.Journal of inflammation research · 2025Article
- Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning.Frontiers in pharmacology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis and septic shock, which are often caused by pneumonia, impact millions of people every year. Despite adequate antibiotic therapy, mortality remains high, up to 45% in septic shock, which is characterized by an inappropriate, excessive immune response of the host. Moreover, critical illness-related corticosteroid insufficiency often coexists. Against this background, several trials and meta-analyses evaluated corticosteroid therapy as adjuvant therapy with heterogeneous results. Indeed, before 2000, high-dosage, short courses of corticosteroid treatment resulted in no benefit on mortality and a higher rate of adverse events. After 2000, thanks to a deeper understanding of the pathophysiology, low-dosage with longer courses of treatment were tested. With this regimen, a faster decrease in inflammation and faster resolution of shock, with a low rate of mild adverse events, was demonstrated although no clear effect on mortality was shown. To date, guidelines on sepsis and septic shock and guidelines on severe community-acquired pneumonia suggest corticosteroid use in selected patients. Furthermore, by utilizing latent class analysis, phenotypes of sepsis patients who benefit the most from corticosteroid treatment were recently identified. Future research should be guided by a precision medicine approach to identify adequate dosage and duration of corticosteroid treatment for appropriate patients. This article is freely available.
Indexed as
Identifiers
What OpenQuestion holds
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.