ArticleFrontiers in medicine2023
Exploring the risk factors of early sepsis after liver transplantation: development of a novel predictive model.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparative analysis of early post-transplant infections after DCD with hypothermic oxygenated liver perfusion and DBD liver transplantation: A retrospective cohort study.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2026Article
- Retrospective Analysis of Risk Factors and Surgical Outcomes in Liver Transplant Recipients.Annals of transplantation · 2026Article
- Epidemiology and Risk Prediction Model of Multidrug-Resistant Organism Infections After Liver Transplant Recipients: A Single-Center Cohort Study.Bioengineering (Basel, Switzerland) · 2025Article
- Individual mHLA-DR trajectories in the ICU as predictors of early infections following liver transplantation: a prospective observational study.Critical care (London, England) · 2025Observational
- Article
- Interpretable machine learning models for pre- and postoperative prediction of early intra-abdominal infections after liver transplantation: a multicenter retrospective cohort study.Therapeutic advances in infectious diseaseArticle
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Authors and funding
10 authors.
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Abstract
Background: Sepsis is a severe and common complication of liver transplantation (LT) with a high risk of mortality. However, effective tools for evaluating its risk factors are lacking. Therefore, this study identified the risk factors of early post-liver transplantation sepsis and established a nomogram. Methods: We analyzed the risk factors of post-liver transplantation sepsis in 195 patients. Patients with infection and a systemic inflammatory response syndrome (SIRS) score ≥ 2 were diagnosed with sepsis. The predictive indicators were screened with the least absolute shrinkage and selection operator (LASSO) and collinearity analyses to develop a nomogram. The prediction performance of the new nomogram model, Sequential Organ Failure Assessment (SOFA) score, and Modified Early Warning Score (MEWS) was compared through assessment of the area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). Results: The nomogram was based on postoperative heart rate, creatinine concentration, PaO Conclusion: Heart rate, creatinine concentration, PaO
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