ArticleClinical and vaccine immunology : CVI2008
Presymptomatic prediction of sepsis in intensive care unit patients.
Article in Clinical and vaccine immunology : CVI, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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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.
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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
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 80 citations in OpenAlex.
- Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Effect of Permissive Underfeeding with Intensive Insulin Therapy on MCP-1, sICAM-1, and TF in Critically Ill Patients.Nutrients · 2019Trial
- The association between body temperature parameters and blood culture positivity in the inpatient setting: a retrospective cohort study.BMC infectious diseases · 2026Article
- Efficient sepsis detection using deep learning and residual convolutional networks.PeerJ. Computer science · 2025Article
- Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool.Cureus · 2024Review
- Abundance ofFrontiers in immunology · 2023Article
- Machine learning models for predicting in-hospital mortality in patient with sepsis: Analysis of vital sign dynamics.Frontiers in medicine · 2022Article
- Transcriptome and Literature Mining Highlight the Differential Expression of ERLIN1 in Immune Cells during Sepsis.Biology · 2021Article
- Plasma interleukin-6 levels correlate with survival in patients with bacterial sepsis and septic shock.Interventional medicine & applied science · 2021Article
- Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.Nature communications · 2021Article
- Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.PloS one · 2021Article
- Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review.Pulmonary therapy · 2020Review
- Predicting weaning difficulty for planned extubation patients with an artificial neural network.Medicine · 2019Article
- Multiple metabolic pathways are predictive of ricin intoxication in a rat model.Metabolomics : Official journal of the Metabolomic Society · 2019Article
- A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit.Biomedical informatics insights · 2019Article
- A Flowthrough Assay for Rapid Bedside Stratification of Bloodstream Bacterial Infection in Critically Ill Patients: a Pilot Study.Journal of clinical microbiology · 2018Article
- An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.Critical care medicine · 2018Observational
- Multiscale network representation of physiological time series for early prediction of sepsis.Physiological measurement · 2017Article
- A vision and a prescription for big data-enabled medicine.Nature immunology · 2015Article
- Applying computation biology and "big data" to develop multiplex diagnostics for complex chronic diseases such as osteoarthritis.Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Postoperative or posttraumatic sepsis remains one of the leading causes of morbidity and mortality in hospital populations, especially in populations in intensive care units (ICUs). Central to the successful control of sepsis-associated infections is the ability to rapidly diagnose and treat disease. The ability to identify sepsis patients before they show any symptoms would have major benefits for the health care of ICU patients. For this study, 92 ICU patients who had undergone procedures that increased the risk of developing sepsis were recruited upon admission. Blood samples were taken daily until either a clinical diagnosis of sepsis was made or until the patient was discharged from the ICU. In addition to standard clinical and laboratory parameter testing, the levels of expression of interleukin-1beta (IL-1beta), IL-6, IL-8, and IL-10, tumor necrosis factor-alpha, FasL, and CCL2 mRNA were also measured by real-time reverse transcriptase PCR. The results of the analysis of the data using a nonlinear technique (neural network analysis) demonstrated discernible differences prior to the onset of overt sepsis. Neural networks using cytokine and chemokine data were able to correctly predict patient outcomes in an average of 83.09% of patient cases between 4 and 1 days before clinical diagnosis with high sensitivity and selectivity (91.43% and 80.20%, respectively). The neural network also had a predictive accuracy of 94.55% when data from 22 healthy volunteers was analyzed in conjunction with the ICU patient data. Our observations from this pilot study indicate that it may be possible to predict the onset of sepsis in a mixed patient population by using a panel of just seven biomarkers.
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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.