ArticleLipids in health and disease2025
Construction of a nomogram for hypertriglyceridemic severe acute pancreatitis that includes metabolic indexes.
Article in Lipids in health and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A prognostic model based on metabolic indicators for recurrent acute pancreatitis in hypertriglyceridemia patients: a retrospective cohort study.Annals of medicine · 2026Article
- Review
- Hypertriglyceridaemia-Associated Acute Pancreatitis: Pathophysiological Insights and Advances in Clinical Management.Biomedicines · 2026Review
- Diagnostic and prognostic value of serum lactate dehydrogenase and amylase in severe acute pancreatitis: A clinical biochemistry-based evaluation.Journal of medical biochemistry · 2026Article
- Development and Validation of a Multidimensional Predictive Model for 28-Day Mortality in Patients with Post-Traumatic Acute Respiratory Distress Syndrome.Journal of clinical medicine · 2026Article
- Prognostic analysis of acute pancreatitis due to different etiologies and establishment and validation of an early prediction model.Lipids in health and disease · 2026Article
- VEZT promotes pancreatic cancer progression by stabilizing SLC25A24 and preserving mitochondrial function.Frontiers in immunology · 2026Article
- Development and Validation of an Early Severity Prediction Model for Hypertriglyceridemia-Associated Acute Pancreatitis: A Multicenter Cohort Study.Journal of inflammation research · 2026Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent scoring systems for hypertriglyceridaemia-induced acute pancreatitis (HTG-AP) severity are few and lack reliability. The present work focused on screening predicting factors for HTG-SAP, then constructing and validating the visualization model of HTG-AP severity by combining relevant metabolic indexes.
methodsBetween January 2020 and December 2024, retrospective clinical information for HTG-AP inpatients from Weifang People's Hospital was examined. CT scans of included patients were evaluated for muscle and fat parameters. To identify independent predictors of HTG-SAP, univariate regression, least absolute contraction and selection operator (LASSO) regression, and multivariable logistic regression were conducted. Meanwhile, the nomogram was created for model visualization, and the model was verified for accuracy, consistency, stability, and utility by calibration, clinical decision curve (DCA), as well as receiver operating characteristic (ROC) analyses.
resultsAltogether 244 HTG-AP patients were enrolled, and they were categorized as a severe group (N = 44) or a non-severe group (N = 200) in line with Atlanta classification criteria. The analysis showed that lactate dehydrogenase (LDH), serum creatinine(Scr), visceral adipose tissue index (VATI), serum albumin(ALB), and triglyceride and glucose (TyG) index independently predicted the HTG-AP severity prediction model, and a nomogram was constructed for visualization, with an internally validated Harrell's consistency index (c-index) of 0.966 (95% CI, 0.943-0.989), besides, calibration curves, ROC, and DCA all revealed that the nomogram had good predictive ability.
conclusionLDH, Scr, VATI, ALB, and TyG independently predict HTG-SAP, and our constructed prediction model has high sensitivity and specificity, which can early identify HTG-AP severity, with a view to giving appropriate interventions to the patients in time, delaying the progression of the patients' conditions, and reducing the complications.
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