ArticleFrontiers in medicine2022
Mortality Prediction in Sepsis With an Immune-Related Transcriptomics Signature: A Multi-Cohort Analysis.
Article in Frontiers in medicine, 2022. 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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- AI-Based Sepsis Prediction in Hospitalized Adults: Systematic Review, Subgroup Meta-Analysis, and Contextual Analysis of Clinical Burden.Journal of medical Internet research · 2026Pooled it
- Metabolic Reprogramming and Immune Metabolism in Sepsis: Targeting the PPAR Pathway for Personalized Therapeutic Approaches.PPAR research · 2026Review
- Advocating for the recognition of underlying immunosuppression in critical illness.EClinicalMedicine · 2025Review
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
- ICU-acquired infections in immunocompromised patients.Intensive care medicine · 2024Review
- Whole blood ratio ofFrontiers in medicine · 2024Article
- Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis.Frontiers in genetics · 2023Review
- Sepsis biomarkers and diagnostic tools with a focus on machine learning.EBioMedicine · 2022Review
- Cytokine Storm-Definition, Causes, and Implications.International journal of molecular sciences · 2022Review
- A 9-mRNA signature measured from whole blood by a prototype PCR panel predicts 28-day mortality upon admission of critically ill COVID-19 patients.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Novel biomarkers are needed to progress toward individualized patient care in sepsis. The immune profiling panel (IPP) prototype has been designed as a fully-automated multiplex tool measuring expression levels of 26 genes in sepsis patients to explore immune functions, determine sepsis endotypes and guide personalized clinical management. The performance of the IPP gene set to predict 30-day mortality has not been extensively characterized in heterogeneous cohorts of sepsis patients. Methods: Publicly available microarray data of sepsis patients with widely variable demographics, clinical characteristics and ethnical background were co-normalized, and the performance of the IPP gene set to predict 30-day mortality was assessed using a combination of machine learning algorithms. Results: We collected data from 1,801 arrays sampled on sepsis patients and 598 sampled on controls in 17 studies. When gene expression was assayed at day 1 following admission (1,437 arrays sampled on sepsis patients, of whom 1,161 were alive and 276 (19.2%) were dead at day 30), the IPP gene set showed good performance to predict 30-day mortality, with an area under the receiving operating characteristics curve (AUROC) of 0.710 (CI 0.652-0.768). Importantly, there was no statistically significant improvement in predictive performance when training the same models with all genes common to the 17 microarray studies ( Conclusion: Using pooled publicly-available gene expression data from multiple cohorts, we showed that the IPP gene set, an immune-related transcriptomics signature conveys relevant information to predict 30-day mortality when sampled at day 1 following admission. Our data also suggests that higher predictive performance could be obtained when assaying gene expression at later time points during the course of sepsis. Prospective studies are needed to confirm these findings using the IPP gene set on its dedicated measurement platform.
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