ArticleInfection and drug resistance2025
Establishment and Validation of a Risk Prediction Model for Sepsis-Associated Liver Injury in ICU Patients: A Retrospective Cohort Study.
Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026Article
- Regulated cell death in sepsis-associated liver injury: molecular mechanisms and therapeutic implications.Frontiers in immunology · 2026Review
- DL-Tryptophan Alleviates Sepsis-Related Acute Liver Injury via AhR Activation.Journal of inflammation research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Sepsis-associated liver injury (SALI) leads to increased mortality in sepsis patients, yet no specialized tools exist for early risk assessment. This study aimed to develop and validate a risk prediction model for early identification of SALI before patients meet full diagnostic criteria. Patients and Methods: This retrospective study analyzed 415 sepsis patients admitted to ICU from January 2019 to January 2022. Patients with pre-existing liver conditions were excluded. Using LASSO regression and multivariate logistic analysis, we developed a predictive nomogram incorporating clinical variables. Model performance was evaluated through internal validation using bootstrapping method. Results: Among the cohort, 97 patients (23.4%) developed SALI. The final model identified five key predictors: total bilirubin, ALT, γ-GGT, mechanical ventilation, and kidney failure. The model demonstrated good discrimination (AUC=0.841, 95% CI: 0.795-0.887) and calibration. Decision curve analysis showed clinical utility across a threshold probability range of 4-87%. The model outperformed traditional scoring systems (SOFA and SAPS II) in predicting SALI risk. Conclusion: This novel nomogram effectively predicts SALI risk in sepsis patients by integrating readily available clinical parameters. While external validation is needed, the model shows promise as a practical tool for early risk stratification, potentially enabling timely interventions in high-risk patients.
Indexed as
Identifiers
What OpenQuestion holds
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.