Observational studyPloS one2021
Clinical and biological clusters of sepsis patients using hierarchical clustering.
Observational study in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06475144 (Effects of an Online Escape Room on Enhancing Nurses' Knowledge, Clinical Reasoning, and Self-Efficacy Regarding Early Warning Signs of Sepsis), which is not on this map. Cited by 15 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.
Effects of an Online Escape Room on Enhancing Nurses' Knowledge, Clinical Reasoning, and Self-Efficacy Regarding Early Warning Signs of Sepsis: A Randomized Controlled Trial
Who cites it
15 citing papers in PubMed, 36 citations in OpenAlex.
- Article
- Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering.BioData mining · 2025Article
- Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.Journal of clinical medicine · 2025Article
- Chemical imaging for biological systems: techniques, AI-driven processing, and applications.Journal of materials chemistry. B · 2025Review
- Bundle compliance patterns in septic shock and their association with patient outcomes: an unsupervised cluster analysis.Internal and emergency medicine · 2025Article
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- A Multivariate Phenotypical Approach of Sepsis and Septic Shock-A Comprehensive Narrative Literature Review.Medicina (Kaunas, Lithuania) · 2024Review
- Clinical phenotypes of sepsis: a narrative review.Journal of thoracic disease · 2024Review
- Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients.Critical care (London, England) · 2024Article
- Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts.Scientific reports · 2024Article
- Clustering based on renal and inflammatory admission parameters in critically ill patients admitted to the ICU.PloS one · 2024Article
- Clinical Sepsis Phenotypes in Critically Ill Patients.Microorganisms · 2023Review
- Time to treatment and mortality for clinical sepsis subtypes.Critical care (London, England) · 2023Article
- Learning and visualizing chronic latent representations using electronic health records.BioData mining · 2022Article
- Lung histopathologic clusters in severe COVID-19: a link between clinical picture and tissue damage.Critical care (London, England) · 2021Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors at 14 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHeterogeneity in sepsis expression is multidimensional, including highly disparate data such as the underlying disorders, infection source, causative micro-organismsand organ failures. The aim of the study is to identify clusters of patients based on clinical and biological characteristic available at patients' admission.
methodsAll patients included in a national prospective multicenter ICU cohort OUTCOMEREA and admitted for sepsis or septic shock (Sepsis 3.0 definition) were retrospectively analyzed. A hierarchical clustering was performed in a training set of patients to build clusters based on a comprehensive set of clinical and biological characteristics available at ICU admission. Clusters were described, and the 28-day, 90-day, and one-year mortality were compared with log-rank rates. Risks of mortality were also compared after adjustment on SOFA score and year of ICU admission.
resultsOf the 6,046 patients with sepsis in the cohort, 4,050 (67%) were randomly allocated to the training set. Six distinct clusters were identified: young patients without any comorbidities, admitted in ICU for community-acquired pneumonia (n = 1,603 (40%)); young patients without any comorbidities, admitted in ICU for meningitis or encephalitis (n = 149 (4%)); elderly patients with COPD, admitted in ICU for bronchial infection with few organ failures (n = 243 (6%)); elderly patients, with several comorbidities and organ failures (n = 1,094 (27%)); patients admitted after surgery, with a nosocomial infection (n = 623 (15%)); young patients with immunosuppressive conditions (e.g., AIDS, chronic steroid therapy or hematological malignancy) (n = 338 (8%)). Clusters differed significantly in early or late mortality (p < .001), even after adjustment on severity of organ dysfunctions (SOFA) and year of ICU admission.
conclusionsClinical and biological features commonly available at ICU admission of patients with sepsis or septic shock enabled to set up six clusters of patients, with very distinct outcomes. Considering these clusters may improve the care management and the homogeneity of patients in future studies.
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