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Liver Volumetry Enhances Prediction of Acute Pancreatitis in Acute Liver Failure: A Nationwide Registry Study
Keisuke Kakisaka1, Nobuaki Nakayama2, Tokio Sasaki1
1Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, Iwate Medical University, Yahaba, Japan.
Background And Aim:
Acute pancreatitis is a recognized complication of acute liver failure (ALF), but its incidence and predictive factors remain poorly defined. Current risk stratification relies primarily on conventional laboratory markers with limited discriminatory ability for pancreatic complications. This study aimed to determine the incidence and prognostic impact of acute pancreatitis in ALF patients and evaluate whether liver volumetry improves prediction beyond conventional markers.
Methods:
This cross-sectional retrospective study utilized data from a nationwide survey-based registry conducted by the Intractable Hepato-Biliary Disease Study Group of Japan (2011-2021). We analyzed 142 ALF patients with available liver volumetry data and 90-day outcome information. Patients with pre-existing acute pancreatitis were excluded. Predictive models were developed comparing conventional markers (MELD score) versus an enhanced model incorporating computed tomography liver volume to standard liver volume ratio (CTLV/SLV). Model performance was evaluated using ROC analysis, net reclassification index, and clinical validation.
Results:
Acute pancreatitis developed in 19 patients (13.4%), typically within the first 8 weeks (84.3% of cases). Logistic regression identified acute pancreatitis as an independent risk factor for poor prognosis (β = 0.2556, p = 0.031). The CTLV/SLV ratio was significantly lower in patients developing pancreatitis (0.80 vs. 1.07, p < 0.001). The enhanced model (MELD + CTLV/SLV) achieved superior discrimination (AUC 0.810) compared to MELD alone (AUC 0.704), with 81% relative risk reduction in the low-risk category.
Conclusion:
Incorporating liver volumetry significantly improves acute pancreatitis prediction in ALF patients, enabling more precise risk stratification and potentially optimizing clinical management strategies.
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