Volume 24 Issue 2
Feb.  2026
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Article Navigation >  Chinese Journal of General Practice  > 2026  >  24(2): 192-195
GAO Ting, FU Hong, SHAO Min, GONG Juan. Establishment of a prediction model for sepsis-related liver injury and development of prevention and control strategies based on Lasso-logistic regression[J]. Chinese Journal of General Practice, 2026, 24(2): 192-195. doi: 10.16766/j.cnki.issn.1674-4152.004358
Citation: GAO Ting, FU Hong, SHAO Min, GONG Juan. Establishment of a prediction model for sepsis-related liver injury and development of prevention and control strategies based on Lasso-logistic regression[J]. Chinese Journal of General Practice, 2026, 24(2): 192-195. doi: 10.16766/j.cnki.issn.1674-4152.004358
shu

Establishment of a prediction model for sepsis-related liver injury and development of prevention and control strategies based on Lasso-logistic regression

doi: 10.16766/j.cnki.issn.1674-4152.004358
  • 1.

    Department of Critical Care Medicine, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China

Funds:

 82370605

  • Received Date: 2025-06-23
    Abstract
  •   Objective  In view of the serious harm of sepsis-related liver injury (SALI), this study aims to use lasso logistic regression to build a SALI prediction model and to develop targeted prevention and control strategies.  Methods  A total of 192 sepsis patients admitted to the First Affiliated Hospital of Anhui Medical University from April 1, 2024 to April 1, 2025 were selected as the research subjects. They were divided into the SALI group (40 cases) and the non SALI group (152 cases) based on whether liver injury occurred. Clinical data from two groups of patients were collected, and univariate analysis was performed to identify factors associated with the occurrence of SALI in sepsis patients. The risk factors of SALI were analyzed by lasso logistic regression, and the SALI risk prediction model was established and verified by ROC curve.  Results  Significant differences were observed between the two groups in the scores of diabetes, mechanical ventilation, shock, hypoxemia, blood urea, platelet count, blood lactic acid, and sequential organ failure assessment (SOFA) scores (P < 0.05). Lasso-logistic regression analysis showed that diabetes mellitus, combined shock, blood lactic acid, and SOFA scores were independent risk factors for SALI (P < 0.05). ROC curve analysis results showed that SALI can be predicted by combining diabetes, combined shock, blood lactic acid, SOFA scores, and the nomogram model were predictive of SALI, with area under the curve values of 0.615, 0.620, 0.843, 0.937, 0.948, respectively. Among these, the nomogram-based prediction model exhibited the highest predictive performance.  Conclusion  Diabetes mellitus, combined shock, blood lactic acid levels, and SOFA score are independent risk factors for SALI. The nomogram model constructed based on these factors shows high predictive accuracy and clinical applicability, and may facilitate the development of accurate prevention and control strategies for SALI patients.

     

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