ArticleScientific data2019
Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
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Who cites it
17 citing papers in PubMed, 29 citations in OpenAlex.
- The Immunologic Effect of Early Intravenous Two and Four Gram Bolus Dosing of Tranexamic Acid Compared to Placebo in Patients With Severe Traumatic Bleeding (TAMPITI): A Randomized, Double-Blind, Placebo-Controlled, Single-Center Trial.Frontiers in immunology · 2020Trial
- Modelling cognitive outcomes in the UK Biobank: Education, noradrenaline and frontoparietal networks.PloS one · 2026Article
- Fecal gut microbiota and amino acids as noninvasive diagnostic biomarkers of Pediatric inflammatory bowel disease.Gut microbes · 2025Article
- Predicting multiple organ failure after major orthopedic interventions is possible using only conventional laboratory tests.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Article
- Development of a biomarker prediction model for post-trauma multiple organ failure/dysfunction syndrome based on the blood transcriptome.Annals of intensive care · 2024Article
- Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations.iScience · 2024Article
- A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.BMC medical informatics and decision making · 2024Article
- Article
- Entering the new digital era of intensive care medicine: an overview of interdisciplinary approaches to use artificial intelligence for patients' benefit.European journal of anaesthesiology and intensive care · 2023Article
- The α7 nicotinic acetylcholine receptor agonist PNU-282987 ameliorates sepsis-induced acute kidney injury through CD4+CD25+ regulatory T cells in rats.Bosnian journal of basic medical sciences · 2022Article
- Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections.Scientific reports · 2021Article
- Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection.Metabolites · 2021Article
- Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India.Microorganisms · 2021Article
- Machine Learning-Driven Metabolomic Evaluation of Cerebrospinal Fluid: Insights Into Poor Outcomes After Aneurysmal Subarachnoid Hemorrhage.Neurosurgery · 2021Article
- Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study.Diagnostics (Basel, Switzerland) · 2021Article
- The potential of fecal microbiota and amino acids to detect and monitor patients with adenoma.Gut microbesArticle
- Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underway and the early drivers of clinical outcome cannot be readily determined. Thus, there is a need to better understand the immediate immune response to injury and how this might influence important patient outcomes such as multi-organ dysfunction syndrome (MODS). In this study, we have assessed the immune response to trauma in 61 patients at three different post-injury time points (ultra-early (<=1 h), 4-12 h, 48-72 h) and analysed relationships with the development of MODS. We developed a pipeline using Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods that were able to identify 3 physiological features (decrease in neutrophil CD62L and CD63 expression and monocyte CD63 expression and frequency) as possible biomarkers for MODS development. After univariate and multivariate analysis for each feature alongside a stability analysis, the addition of these 3 markers to standard clinical trauma injury severity scores yields a Generalized Liner Model (GLM) with an average Area Under the Curve value of 0.92 ± 0.06. This performance provides an 8% improvement over the Probability of Survival (PS14) outcome measure and a 13% improvement over the New Injury Severity Score (NISS) for identifying patients at risk of MODS.
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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.