ReviewMicroorganisms2023
Clinical Sepsis Phenotypes in Critically Ill Patients.
Review in Microorganisms, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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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
33 citing papers in PubMed, 38 citations in OpenAlex.
- Phenotype-based appropriateness of IGM-enriched polyclonal immunoglobulin use in critical illness: report of an expert consensus.Journal of anesthesia, analgesia and critical care · 2026Review
- Physiological Phenotypes in Comatose ICU Patients: A Retrospective Multidimensional Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Comorbidities and Inflammation: How Chronic Diseases Prime the Host Response in Sepsis.International journal of molecular sciences · 2026Review
- Update on sepsis treatment.Journal of intensive care · 2026Review
- Corticosteroids in immunocompromised ICU patients with pneumonia: risks, evidence, and clinical practice.Pneumonia (Nathan Qld.) · 2026Review
- Integrating host mRNA signatures and clinical trajectories with machine learning for risk stratification and survival prediction in sepsis: an ICU-based prospective cohort study.BMC infectious diseases · 2026Article
- Development and Validation of a Nomogram for Mortality Prediction in Septic Patients with Prolonged or Chronic Critical Illness.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis.Journal of clinical medicine · 2026Review
- Artificial intelligence-driven cluster analysis for identifying clinical phenotypes in suspected sepsis patients in the emergency department.BMC emergency medicine · 2026Article
- Prediction-guided clustering for sepsis phenotyping: a retrospective cohort analysis.Intensive care medicine experimental · 2026Article
- Acquisition Origin Matters: Clinical, Microbiological and Immunological Characteristics and Treatment Effects in Community- vs. Hospital-Acquired Septic Shock.Antibiotics (Basel, Switzerland) · 2026Article
- Machine Learning Model for Sepsis Prediction in Prolonged and Chronic Critical Illness: Development and Validation Using Retrospective Real-World ICU Data.Journal of clinical medicine · 2026Article
- Derivation of a novel clinical phenotype of sepsis in critically ill patients: a secondary analysis of a multicentre, prospective, observational study.Frontiers in medicine · 2026Article
- Relation between initial hypothermia, course of the hypothermia and mortality in patients with septic shock: a post-hoc analysis of the SEPSISPAM randomized trial.Annals of intensive care · 2026Article
- The effectiveness of tissue-perfusion-guided resuscitation in shock: A systematic review and meta-analysis.Annals of intensive care · 2026Review
- Quality assessment of clinical practice guidelines for sepsis and variations in recommendations.BMC infectious diseases · 2025Article
- Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome.Infection · 2025Article
- Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.Journal of clinical medicine · 2025Article
- Biomarkers as Beacons: Illuminating Sepsis-Associated Hepato-Renal Injury.International journal of molecular sciences · 2025Review
- Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China.Scientific reports · 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
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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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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.