ReviewFrontiers in immunology2018
Arguing for Adaptive Clinical Trials in Sepsis.
Review in Frontiers in immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.Intensive care medicine · 2020Pooled 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
- Pediatric sepsis-associated acute kidney injury: leveraging pathophysiology for risk stratification and identification of treatable traits.Pediatric nephrology (Berlin, Germany) · 2026Review
- Multi-omic and computational approaches for biomarker discovery and clinical translation in pediatric sepsis.Frontiers in pharmacology · 2026Review
- The Role of Pyroptosis in Exacerbating Sepsis-Induced Myocardial Injury: Mechanisms and Implications.Journal of inflammation research · 2026Review
- An In-Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single-Cell RNA Sequencing Analyses.Global challenges (Hoboken, NJ) · 2025Article
- An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study.Journal of medical Internet research · 2025Article
- Clinical subtypes in critically ill patients with sepsis: validation and parsimonious classifier model development.Critical care (London, England) · 2025Article
- Article
- The Global Burden of Sepsis and Septic Shock.Epidemiologia (Basel, Switzerland) · 2024Review
- Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis.Interactive journal of medical research · 2024Article
- Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes.Intensive care medicine · 2023Observational
- Dynamics of ventilatory pattern variability and Cardioventilatory Coupling during systemic inflammation in rats.Frontiers in network physiology · 2023Article
- A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department.BMC emergency medicine · 2022Article
- Online control of the False Discovery Rate in group-sequential platform trials.Statistical methods in medical research · 2022Article
- Study designs for clinical trials applied to personalised medicine: a scoping review.BMJ open · 2022Article
- Current Clinical Trials in Traumatic Brain Injury.Brain sciences · 2022Review
- Randomised clinical trials in critical care: past, present and future.Intensive care medicine · 2022Review
- Chasing the Ghost: Hyperinflammation Does Not Cause Sepsis.Frontiers in pharmacology · 2022Article
- Clinical phenotypes of acute kidney injury are associated with unique outcomes in critically ill septic children.Pediatric research · 2021Observational
Corrections and comments
- Commented on by
Authors and funding
4 authors.
Funding
Abstract
Sepsis is life-threatening organ dysfunction due to dysregulated response to infection. Patients with sepsis exhibit wide heterogeneity stemming from genetic, molecular, and clinical factors as well as differences in pathogens, creating challenges for the development of effective treatments. Several gaps in knowledge also contribute: (i) biomarkers that identify patients likely to benefit from specific treatments are unknown; (ii) therapeutic dose and duration is often poorly understood; and (iii) short-term mortality, a common outcome measure, is frequently criticized for being insensitive. To date, the majority of sepsis trials use traditional design features, and have largely failed to identify new treatments with incremental benefit over standard of care. Traditional trials are also frequently conducted as part of a drug evaluation process that is segmented into several phases, each requiring separate trials, with a long time delay from inception through design and execution to incorporation of results into clinical practice. By contrast, adaptive clinical trial designs facilitate the evaluation of several candidate treatments simultaneously, learn from emergent discoveries during the course of the trial, and can be structured efficiently to lead to more timely conclusions compared to traditional trial designs. Adoption of new treatments in clinical practice can be accelerated if these trials are incorporated in electronic health records as part of a learning health system. In this review, we discuss challenges in the evaluation of treatments for sepsis, and explore potential benefits and weaknesses of recent advances in adaptive trial methodologies to address these challenges.
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.