Analysis of risk factors for sepsis-related liver injury and construction of a prediction model
Bibliographic Data
| ID | 22086664 |
|---|---|
| Authors | Yong He (0000-0002-8303-9570, Sichuan University), Chi Wang (0000-0001-5610-5547, Sichuan University), Wan He (0000-0002-4619-5013, Sichuan University), He Zhang (0009-0009-4236-9627, Sichuan University), Fei Ding (0009-0004-9087-2415, Sichuan University), Ying Liu (0000-0001-5584-7805, Sichuan University), He He (0000-0002-1513-3987, Sichuan University), Binwu Ying (0000-0001-8077-3908, Sichuan University), Xin Nie (0000-0003-2759-2950, Sichuan University, corresponding author) |
| Year | 2024 |
| Volume | 12 |
| Pages | 1475292-1475292 |
| Publication date | 2024-12-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1475292 |
| PMID | 39712312 |
| OpenAlex | W4405123014 |
| Language | EN |
| References cited | 37 |
Background: Sepsis is a leading cause of mortality in critically ill patients, and the liver is a key organ affected by sepsis. Sepsis-related liver injury (SRLI) is an independent risk factor for multiple organ dysfunction syndrome (MODS) and mortality. However, there is no clear diagnostic standard for SRLI, making early detection and intervention challenging. Objective: This study aimed to investigate the predictive value of serum indices for the occurrence of SRLI in adults to guide clinical practice. Methods: In this study, we investigated the predictive value of serum indices for SRLI in adults. We retrospectively analyzed data from 1,573 sepsis patients admitted to West China Hospital, Sichuan University, from January 2015 to December 2019. Patients were divided into those with and without liver injury. Stepwise logistic regression identified independent risk factors for SRLI, and a predictive model was constructed. The model's diagnostic efficacy was assessed using receiver operating characteristic (ROC) curve analysis. Results: -CP), antithrombin III (AT III), fibrin/fibrinogen degradation products (FDP), and red blood cell distribution width (RDW-CV) were independent predictors of SRLI. The area under the curve (AUC) of the predictive model was 0.890, with a sensitivity of 80.0% and a specificity of 82.91%, indicating excellent diagnostic value. Conclusion: In conclusion, this study developed a highly accurate predictive model for SRLI using clinically accessible serum indicators, which could aid in early detection and intervention, potentially reducing mortality rates
Intensive care medicine · Medical emergency · Sepsis · Blood properties and coagulation · Computer Science · Medicine · Sepsis Diagnosis and Treatment · Trauma, Hemostasis, Coagulopathy, Resuscitation · Internal Medicine
| Highly cited | No |
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