ReviewIntensive care medicine2024
Causal inference can lead us to modifiable mechanisms and informative archetypes in sepsis.
Review in Intensive care medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Rationalizing Heterogeneity in Staphylococcus aureus Bacteremia: Current Progress and Future Goals.The Journal of infectious diseases · 2026Review
- Reproducible bicarbonate thresholds predict critically ill patient mortality in an international personalized survival study.iScience · 2026Article
- Reproducible Identification of Staphylococcus aureus Bacteremia Clinical Subphenotypes.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Observational
- Novel Susceptibility Genes for Sepsis Revealed by a Cross-Tissue Transcriptome-Wide Association Study.Shock (Augusta, Ga.) · 2026Article
- Update on sepsis and septic shock: from bundles to precision medicine.Intensive care medicine · 2026Article
- Data-driven spatial-temporal framework for exploring the heterogeneity and temporality of sepsis.Chinese medical journal · 2026Review
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- Unlocking host response signatures in sepsis and critical illnesses.Nature medicine · 2025Article
- On the tightrope between life and graft: immunosuppression in critically ill solid organ transplant recipients.Intensive care medicine · 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
21 authors.
Funding
Abstract
Medical progress is reflected in the advance from broad clinical syndromes to mechanistically coherent diagnoses. By this metric, research in sepsis is far behind other areas of medicine-the word itself conflates multiple different disease mechanisms, whilst excluding noninfectious syndromes (e.g., trauma, pancreatitis) with similar pathogenesis. New technologies, both for deep phenotyping and data analysis, offer the capability to define biological states with extreme depth. Progress is limited by a fundamental problem: observed groupings of patients lacking shared causal mechanisms are very poor predictors of response to treatment. Here, we discuss concrete steps to identify groups of patients reflecting archetypes of disease with shared underlying mechanisms of pathogenesis. Recent evidence demonstrates the role of causal inference from host genetics and randomised clinical trials to inform stratification analyses. Genetic studies can directly illuminate drug targets, but in addition they create a reservoir of statistical power that can be divided many times among potential patient subgroups to test for mechanistic coherence, accelerating discovery of modifiable mechanisms for testing in trials. Novel approaches, such as subgroup identification in-flight in clinical trials, will improve efficiency. Within the next decade, we expect ongoing large-scale collaborative projects to discover and test therapeutically relevant sepsis archetypes.
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.