Derivation and validation of prognostic models for predicting survival outcomes in Acute‐on‐chronic liver failure patients

Rui, Fajuan; Yang, Hai; Guo, Zhaoyang; Ge, Zhengming; Hu, Xinyu; Zhang, Lulu; Xue, Qi; Chen, Haiping; Xu, Yayun; Tan, Meng H.; Qin, Chengyong; He, Zhike; Li, Jie

doi:10.1111/jvh.13611

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…INR and total cholesterol are important indicators of liver synthetic function. Similar to previous studies (15,16), these 2 indicators in this study were independent risk factors of mortality in patients with subacute liver failure. Therefore, it is necessary to observe the dynamic changes of these 2 indexes in the clinic and implement timely and effective interventions.…”

Section: Discussionsupporting

confidence: 91%

Risk factors and prognostic model for HBV-related subacute liver failure

Yang

et al. 2022

Ann Transl Med

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Background:The prognosis for patients with chronic hepatitis B virus (HBV)-related subacute liver failure is poor. Thus, accurate prognostication would facilitate management and optimize liver allocation. This study aimed to explore the risk factors for HBV-related subacute liver failure and establish a risk model. Methods: A total of 192 patients with HBV-related subacute liver failure treated at the First Affiliated Hospital of Xi'an Jiaotong University during January 2018 to January 2019 were selected and divided into the survival group (n=113) and the death group (n=79) based on their status within 6 months. Patient information were collected, including age, sex, body mass index, complications, hepatitis B e antigen (HBeAg), hepatic encephalopathy, hepatorenal syndrome, infections, ascites, HBV-DNA, Model for End-Stage Liver Disease (MELD), liver function tests, international normalized ratio (INR), serum creatinine and total cholesterol. Binary logistic regression was employed to identify risk factors for risk model establishment. The predictive value of the risk model was assessed with a receiver operating characteristic (ROC) curve. Results: Compared with the survival group, the patient age, incidence of hepatic encephalopathy and hepatorenal syndrome, infection and ascites rate, MELD score, and alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), INR, and serum creatinine levels were significantly elevated, whereas the total cholesterol level was significantly decreased in the death group (all P<0.05). Patient age [odds ratio (OR) =1.11, P=0.03], hepatic encephalopathy (OR =8.31, P=0.02), infection (OR =4.27, P=0.005), ascites (OR =4.54, P=0.006), MELD score (OR =1.39, P<0.001), INR (OR =5.89, P=0.001), and total cholesterol (OR =0.31, P=0.002) were identified as prognostic factors affecting patient mortality. Although both the MELD score and the risk model established in the present study could differentiate patient outcomes, the area under the curve (AUC) (0.94 vs. 0.82, P<0.001) and sensitivity (91.1% vs. 58.2%, P<0.001) of the established risk model were significantly higher than those of the MELD score. Conclusions: Patient age, hepatic encephalopathy, infection, ascites, MELD score, INR, and total cholesterol level were independent prognostic factors. The prognostic model established based on these risk factors may have favorable predictive value.

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Section: Discussionsupporting

confidence: 91%

Risk factors and prognostic model for HBV-related subacute liver failure

Yang

et al. 2022

Ann Transl Med

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“…Additionally, a few predictive models based on the immune-inflammatory and hepatic function indexes were also constructed to predict the infection development and prognosis in ACLF with limited predictive efficacy. Zhang Z and Rui et al reported that two predictive models (one named GIC including serum globulin, IL-6, and CRP; the other model including albumin, CRP, and glucose) can predict the bacterial infection development in HBV-ACLF patients 6 , 7 . Furthermore, some parameters, such as age, total bilirubin, lactate dehydrogenase, soluble IL-2 receptor, WBC count, etc., were also identified as independent predictors for mortality in ACLF patients with infection 8 , 9 .…”

Section: Introductionmentioning

confidence: 99%

A novel laboratory-based nomogram for assessing infection presence risk in acute-on-chronic liver failure patients

Sun,

Lu,

Ren

et al. 2023

Sci Rep

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Accurate assessment of infection presence risk level, timely diagnosis, and effective control are critical for decreasing mortality of Acute‑on‑chronic liver failure (ACLF). We aimed to develop and validate a novel diagnostic model to accurately assess infection presence risk level in ACLF patients. 185 ACLF patients with/without infection were enrolled, and their demographic, physical findings, immune-inflammatory, hepatic function, metabolism, and coagulation-fibrinolysis indicators were analyzed. Regression analysis was performed to identify the independent diagnostic parameters, which were further used to establish diagnostic models with a nomogram for visual. An area under receiver operating characteristic curve (AUROC), calibration plots, clinical impact curves, decision curve analysis, and net reclassification index were used to evaluate and identify the best model. An external validating cohort was introduced to verify the diagnostic accuracy. We screened out white blood cell (WBC) count, LYM%, blood urea nitrogen (BUN), and D-dimer for assessing infection presence risk levels in ACLF patients. WBD (WBC + BUN + D-dimer) was established and proposed as a novel diagnostic model for infection presence risk levels assessment in ACLF patients with an AUROC of 0.803 (95%CI 0.723–0.883), 0.885 (95%CI 0.786–0.984) in training and external cohorts, respectively. In stratification analysis by ACLF etiology and stages, WBD achieved an AUROC of 0.791 (95%CI 0.691–0.891) and 0.873 (95%CI 0.78–0.966) in HBV-related and early-stage patients, respectively. Whereas a higher AUROC of 0.905 (95%CI 0.807–1.00) in the early-stage of HBV-related ACLF patients indicated its optimum application scope. WBD, a novel laboratory-based nomogram, can serve as a decision-making support tool for clinicians to assess infection presence risk levels in ACLF patients.

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