Clinical characteristics and construction of a predictive model for patients with sepsis related liver injury

Xie, Tonghui; Xin, Qi; Cao, Xirong; Chen, Rui; Ren, Hong; Liu, Chang; Zhang, Jingyao

doi:10.1016/j.cca.2022.10.004

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…However, inadequate local perfusion due to ischemia and shock, cholestasis, and drug toxicity make the liver a target organ for sepsis-related injury [7] . A total of 196 (17.6%) of the 1116 patients with sepsis developed SALI recruited by Xie T [8] et al The results of the present study showed that the incidence of SALI was about 12.48%, which is similar to it. This study showed that the incidence of SALI was lower than that of acute respiratory failure (48.38%), acute heart failure (20.12%), and acute renal failure (31.96%).…”

Section: Discussionsupporting

confidence: 81%

Construction of a predictive model for the risk of sepsis-related liver injury in the intensive care unit: a retrospective cohort study over 10 years

Lin,

Lin

et al. 2024

Preprint

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OBJECTIVE: Sepsis-associated liver injury (SALI) seriously affects the prognosis of patients in the intensive care unit, and there is still a lack of effective predictive tools. The aim of this study was to investigate the independent risk factors of SALI and to establish and validate an effective risk prediction model. METHODS: Patients diagnosed with sepsis by the Medical Information Marketplace for Intensive Care (MIMIC-IV v2.2) were included. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to identify independent risk factors for developing SALI. Risk prediction models were constructed and column-line plots were drawn. The predictive model was validated using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (H-L) test and decision curve analysis (DCA). RESULTS: A total of 8549 sepsis patients were included in the final cohort. There were 4834 males and 3715 females; the mean age of the patients was 66.72 ± 16.18. A total of 1067 (12.48%) patients developed SALI. All patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was analyzed using LASSO logistic regression with the final inclusion of eight indicators used to construct the predictive model: abdominal infection (OR =4.046,95% CI:3.236-5.059, P<0.001), vasopressin (OR=2.188, 95% CI:1.778-2.694 , P<0.001), continuous renal replacement therapy (CRRT) (OR=1.928, 95%CI:1.506-2.469, P<0.001), red blood cell distribution width (RDW) (OR=1.109, 95%CI:1.069-1.151, P<0.001), platelet ( OR=0.996, 95%CI:0.996-0.996, P< 0.001), prothrombin time (PT) (OR=1.042, 95%CI:1.034-1.051, P<0.001), lactate (OR=1.154, 95%CI:1.107-1.202 ,P<0.001) and SOFA score (OR=1.119, 95%CI:1.089-1.150 ,P <0.001). The area under the ROC curve (AUC) of the predictive model was 0.830 (95% CI:0.814-0.847) in the training cohort and 0.847 (95% CI:0.825-0.869) in the validation cohort. The calibration curves were in good agreement with the ideal curves in both the training cohort and the validation cohort. Decision curve analysis (DCA) showed the ability to obtain a net benefit from this model with good clinical applicability. CONCLUSION: Abdominal infection, vasopressin, CRRT, RDW, platelet, PT, lactate, and SOFA score are independent risk factors for patients with SALI. The SALI prediction model constructed based on the above factors has certain clinical predictive value.

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Section: Discussionsupporting

confidence: 81%

Construction of a predictive model for the risk of sepsis-related liver injury in the intensive care unit: a retrospective cohort study over 10 years

Lin,

Lin

et al. 2024

Preprint

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“…Various models have been developed to evaluate the prognostic risks associated with sepsis and its complications. For instance, studies utilizing stepwise logistic regression have analyzed independent risk factors for sepsis-related liver injury, demonstrating the models' effectiveness through ROC curves with promising results [ 7 ]. Similarly, predictive models for sepsis-associated acute kidney injury have shown strong predictive performance in both training and validation cohorts, especially in elderly patients [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A novel clinical prediction model for in-hospital mortality in sepsis patients complicated by ARDS: A MIMIC IV database and external validation study

Chen,

Zong,

Zou

et al. 2024

Heliyon

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“…Studies have indicated that APRI and FIB-4 have potential predictive values in the prognosis of many diseases, including non-infectious and infectious diseases. 9 , 10 Recent evidence indicates that liver fibrosis scores can predict the risks of cardiovascular mortality in coronary artery disease. 9 A previous study indicated that the APRI can accurately and effectively predict the occurrence of sepsis-related liver injury.…”

Section: Introductionmentioning

confidence: 99%

“… 9 A previous study indicated that the APRI can accurately and effectively predict the occurrence of sepsis-related liver injury. 10 FIB-4 levels have also been associated with cardiovascular disease severity in patients with metabolic dysfunction-associated fatty liver disease. 11 …”

Section: Introductionmentioning

confidence: 99%

Liver Fibrosis Scores as Predictors of Blood-Culture-Positive and Sepsis in Patients with Klebsiella pneumoniae Liver Abscess

Zhou,

Ye,

2024

IDR

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Objective Klebsiella pneumoniae liver abscess (KPLA) is an invasive infectious disease with a considerable prevalence and complications. This study aimed to determine the predicted value of aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) for positive blood cultures and sepsis in patients with KPLA. Methods We evaluated 248 consecutive participants diagnosed with KPLA. Demographic characteristics, clinical features, and laboratory test results of the participants were recorded. Multivariate logistic regression analysis was performed to identify the risk factors. Receiver operating characteristic (ROC) analyses were conducted to evaluate the discriminatory ability of APRI and FIB-4. Results 30.2% (75 of 248) KPLA patients presented with positive blood cultures, and 70 (28.2%) developed sepsis. Among the positive blood culture and sepsis groups, the APRI and FIB-4 showed statistically significant increases. Multivariate analysis showed that APRI (odds ratio [OR] = 1.190, 95% confidence interval [CI] 1.051–1.347, p = 0.006) and FIB-4 (OR = 1.110, 95% CI 1.046–1.179, p = 0.001) were independent prognostic factors for positive blood cultures. Both APRI (OR = 1.505, 95% CI 1.149-1.988, p = 0.004) and FIB-4 (OR = 1.187, 95% CI 1.054-1.336, p = 0.005) were independent risk factors for sepsis. The area under the ROC curve (AUC) of APRI and FIB-4 for predicting positive blood cultures of KPLA was 0.783 and 0.766, respectively. APRI had an AUC of 0.801, with a sensitivity and specificity of 71.4% and 81.5%, respectively, for predicting sepsis in patients with KPLA, and the prediction performance of APRI was better than that of FIB-4 (AUC = 0.798). Conclusion In our study, APRI and FIB-4 are effective methods for predicting KPLA patients with positive blood cultures and sepsis.

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Clinical characteristics and construction of a predictive model for patients with sepsis related liver injury

Cited by 9 publications

References 31 publications

Construction of a predictive model for the risk of sepsis-related liver injury in the intensive care unit: a retrospective cohort study over 10 years

Construction of a predictive model for the risk of sepsis-related liver injury in the intensive care unit: a retrospective cohort study over 10 years

A novel clinical prediction model for in-hospital mortality in sepsis patients complicated by ARDS: A MIMIC IV database and external validation study

Liver Fibrosis Scores as Predictors of Blood-Culture-Positive and Sepsis in Patients with Klebsiella pneumoniae Liver Abscess

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