ArticlePeerJ2025
Development of a predictive model for severe hyperlipidemic acute pancreatitis based on LASSO regression: a retrospective study.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Dietary Predictors of Paraben Exposure Among Adults in Northern Thailand.International journal of environmental research and public health · 2026Article
- Association between peak D-dimer levels within 24 hours of admission and the risk of infected pancreatic necrosis in hyperlipidemic acute pancreatitis: a retrospective analysis.Frontiers in medicine · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In recent years, the incidence of hyperlipidemic acute pancreatitis (HLAP) has been increasing. Identifying the risk factors associated with severe HLAP and developing a predictive model are crucial for early detection and intervention, thereby alleviating the disease burden. This study aimed to investigate the risk factors associated with severe HLAP and develop a predictive model. Methods: Data on HLAP treated in Taixing People's Hospital Affiliated to Yangzhou University from January 1, 2020, to June 30, 2023, were retrospectively collected and divided into a mild group ( Results: The univariate analysis showed statistically significant differences in 50 variables between the mild and moderately severe/severe groups. LASSO regression identified the following variables: D-dimer, blood calcium, cholesterol, standard bicarbonate (SB), total carbon dioxide, and C-reactive protein-albumin ratio (CAR). The constructed logistic regression model included D-dimer, blood calcium, and cholesterol, with an AUC of 0.8341 (95% CI [0.7724-0.8958]). The model's calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test ( Conclusion: The risk factors of severe HLAP include D-dimer elevation, calcium depletion and cholesterol elevation. The predictive model established by logistic regression has good performance, which is helpful for early identification and intervention by clinicians.
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Registered trials
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