Chang Li,1,* Jinling Ji,1,* Ting Shi,2 Shennan Pan,1 Kun Jiang,1 Yuzhang Jiang,1,* Kai Wang3,* 

1Department of Medical Laboratory, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, People’s Republic of China; 2Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, People’s Republic of China; 3Department of Immunology and Rheumatology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Kai Wang, Department of Immunology and Rheumatology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, No. 6 Beijing West Road, Huaiyin District, Huaian, Jiangsu, People’s Republic of China, Tel +8613770351754, Email morrosun@hotmail.com

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.

Keywords: SALI, variable, nomogram, risk, probability