ArticleRenal failure2026
A multimodal predictive model incorporating transcriptomic-guided blood biomarkers and clinical variables for sepsis-associated acute kidney injury.
Article in Renal failure, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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Who cites it
1 citing paper in PubMed.
- Letter to the Editor on: "Two-Year Pathogen Dynamics, Coinfection Features, and Core Risk Factors Associated With Severe Pediatric Acute Respiratory Tract Infections in Hangzhou, China".Influenza and other respiratory viruses · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis-associated acute kidney injury (SA-AKI) is a major complication in the intensive care unit (ICU), and early risk stratification remains challenging. This study developed a multimodal predictive model integrating clinical variables with transcriptomic-guided blood biomarkers. Differentially expressed genes were identified from the Gene Expression Omnibus (GEO) database, and cluster of differentiation 177 (CD177) and interleukin-18 receptor 1 (IL18R1) were identified as immune-activation biomarkers associated with SA-AKI. In a retrospective cohort of 188 septic patients (89 SA-AKI), gene expression was quantified by quantitative polymerase chain reaction (qPCR) using blood samples collected within 24 h of ICU admission. Clinical predictors including the Sequential Organ Failure Assessment (SOFA) score, mean arterial pressure (MAP), blood urea nitrogen (BUN), C-reactive protein (CRP), and mechanical ventilation were incorporated using least absolute shrinkage and selection operator (LASSO) feature selection and logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision-curve analysis in an internal test cohort and an external cohort (
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