Observational studyIntensive care medicine2023
Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes.
Observational study in Intensive care medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 3 of them syntheses that pooled 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.
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
92 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Mitochondrial dysfunction in sepsis: nutritional strategies for restoring bioenergetic homeostasis.Frontiers in cellular and infection microbiology · 2026Pooled it
- A consensus blood transcriptomic framework for sepsis.Nature medicine · 2025Guideline
- Predictive enrichment using biomarkers in studies of critically-ill patients with sepsis: a systematic review.Critical care (London, England) · 2025Pooled it
- Relationship Between Phenotyping and Individualized Absolute Risk Differences in Sepsis: A Secondary Analysis of Two Approaches in Two Multicenter Trials.Critical care explorations · 2025Trial
- Heterogeneity of treatment effect of vilobelimab in COVID-19: a secondary analysis of a randomised controlled trial.Critical care (London, England) · 2024Trial
- Identification of a hyperinflammatory sepsis phenotype using protein biomarker and clinical data in the ProCESS randomized trial.Scientific reports · 2024Trial
- MAFG as a Redox-Sensitive Node Linking Peripheral Immune Reprogramming to Cognitive Risk in the Sepsis-to-Alzheimer's Disease Continuum: Single-Cell Dissection and Adoptive-Transfer Validation.CNS neuroscience & therapeutics · 2026Article
- Dynamic Co-Evolution of C-Reactive Protein and Albumin in Early Sepsis: A Group-Based Dual-Trajectory Analysis for Risk Stratification.Journal of cellular and molecular medicine · 2026Article
- FPR1 drives sepsis-associated cognitive impairment through region-specific transcriptional and splicing dysregulation in the brain.Metabolic brain disease · 2026Article
- Approximating Molecular Sepsis Subtypes Using Bedside Data in Resource-Limited Settings: A Multicenter Analysis From Uganda.Critical care explorations · 2026Observational
- Multi-cohort consensus clustering identifies three distinct transcriptomic endotypes in sepsis.BMC bioinformatics · 2026Article
- The molecular ICU: a primer on omics, informatics and the future of precision critical care.Critical care (London, England) · 2026Review
- Service-Specific Heterogeneity in Sepsis Variable Significance and Machine Learning Model Performance: A Stratified Analysis of the BIAlert Cohort.Journal of clinical medicine · 2026Article
- The role of hemoadsorption in septic shock: toward a personalized approach.Critical care (London, England) · 2026Review
- Multicenter Validation of Clinical Sepsis Phenotypes.JAMA network open · 2026Article
- Association between CRP rs1800947 genotypes, dexamethasone use, postoperative CRP level and morbidity in adult cardiac surgical patients in post-hoc analysis of the observational INFLACOR cohort trial.Scientific reports · 2026Observational
- Association between blood urea nitrogen trajectory phenotypes and prognosis in patients with sepsis: a multicenter retrospective cohort study.BMC infectious diseases · 2026Observational
- Development and validation of clinical phenotypes in sepsis-associated liver injury using multi-algorithm consensus classification: a retrospective cohort study.BMC infectious diseases · 2026Article
- Novel genetic insights into causal effects of potential metformin targets and immune mediation on sepsis.Biology direct · 2026Article
- Assessment of Bedside-Adaptable Models to Predict Molecular Sepsis Subtypes in a Resource-Limited Setting: A Multicenter Analysis from Uganda.medRxiv : the preprint server for health sciences · 2026Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
purposeThe heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies.
methodsThis secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, β, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles.
resultsAll four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made.
conclusionAmong critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.
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.