ReviewJournal of thoracic disease2024
Clinical phenotypes of sepsis: a narrative review.
Review in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Lymphopenia and elevated NLR identify patients with septic shock who may benefit from corticosteroids: a target trial emulation study.Intensive care medicine experimental · 2026Article
- Epidemiology and outcomes of adult (non-maternity) community-onset sepsis in Ireland: a secondary analysis of acute hospital administrative data 2020-2024.Irish journal of medical science · 2026Article
- Prediction-guided clustering for sepsis phenotyping: a retrospective cohort analysis.Intensive care medicine experimental · 2026Article
- Targeting the Gut in Sepsis: Therapeutic Potential of Medical Gases.Biomolecules · 2026Review
- An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study.NPJ digital medicine · 2026Article
- Article
- Ethyl palmitate ameliorates lethal endotoxemia by inducing hepatic fetuin-A secretion: anJournal of thoracic disease · 2024Article
- A Bayesian genomic-enhanced precision critical care early warning model for septic shock outcomes.International journal of critical illness and injury scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Sepsis, characterized by an aberrant immune response to infection leading to acute organ dysfunction, impacts millions of individuals each year and carries a substantial risk of mortality, even with prompt care. Despite notable medical advancements, managing sepsis remains a formidable challenge for clinicians and researchers, with treatment options limited to antibiotics, fluid therapy, and organ-supportive measures. Given the heterogeneous nature of sepsis, the identification of distinct clinical phenotypes holds the promise of more precise therapy and enhanced patient care. In this review, we explore various phenotyping schemes applied to sepsis. Methods: We searched PubMed with the terms "Clinical phenotypes AND sepsis" for any type of article published in English up to September 2023. Only reports in English were included, editorials or articles lacking full text were excluded. A review of clinical phenotypes of sepsis is provided. Key Content and Findings: While discerning clinical phenotypes may seem daunting, the application of artificial intelligence and machine learning techniques provides a viable approach to quantifying similarities among individuals within a sepsis population. These methods enable the differentiation of individuals into distinct phenotypes based on not only factors such as infectious diseases, infection sites, pathogens, body temperature changes and hemodynamics, but also conventional clinical data and molecular omics. Conclusions: The classification of sepsis holds immense significance in improving clinical cure rates, reducing mortality, and alleviating the economic burden associated with this condition.
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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.