ArticleFrontiers in medicine2024
Development and validation of a nomogram combining pain score with laboratory indicators for predicting persistent organ failure in acute pancreatitis: a retrospective cohort study.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Clinical-radiological comparative evaluation of the BISAP and Computed tomography severity index scores as predictors of severity in acute pancreatitis.Pakistan journal of medical sciences · 2026Article
- Development of a prediction method for severe pancreatitis using a nomogram.Frontiers in medicine · 2026Article
- Extracellular vesicle therapy for acute pancreatitis: experimental validation of mesenchymal stem cell-derived nanovesicles.BMC pharmacology & toxicology · 2025Article
- Stress-Induced Hyperglycemia as an Independent Predictor of Infectious Pancreatic Necrosis in Acute Pancreatitis: A Machine Learning-Driven Prognostic Model.Journal of inflammation research · 2025Article
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5 authors.
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Abstract
Background: Acute pancreatitis is an inflammatory disease that can lead to persistent organ failure (POF), which is associated with increased morbidity and mortality. Early prediction of POF in AP can significantly improve patient outcomes. Objective: To develop and validate a nomogram that combines pain score with laboratory indicators for predicting POF in patients with AP. Methods: A retrospective cohort study was conducted, including patients diagnosed with AP. Pain score and laboratory indicators were collected within the first 24 h of admission. A nomogram was developed using logistic regression models and validated in a separate cohort. Results: There were 807 patients in the training cohort and 375 patients in the internal validation cohort.Multivariate logistic regression demonstrated that pain score, serum creatinine, hematocrit, serum calcium, and serum albumin were independent risk factors for the incidence of POF in patients with AP. The area under the curve of the nomogram constructed from the above factors were 0.924, respectively. The model demonstrated good calibration and discrimination in both the development and validation cohorts. Conclusion: The nomogram had a good performance in predicting POF in patients with AP and can be used to guide clinical decision-making.
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