ArticleInternational journal of general medicine2026
A Clinical Data-Based Nomogram Prediction Model for ARDS in Patients With Acute Pancreatitis.
Article in International journal of general medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Acute pancreatitis (AP) is a common gastrointestinal emergency that may rapidly progress to severe disease. Acute respiratory distress syndrome (ARDS) is one of the most fatal complications of AP, yet reliable early prediction tools remain limited. Early identification of high-risk patients may improve clinical outcomes. Purpose: To develop and validate a nomogram prediction model for AP complicated by ARDS based on clinical data. Methods: A total of 280 AP patients admitted to our hospital between February 2022 and March 2024 were retrospectively enrolled as the training set, and 129 patients admitted between April 2024 and June 2025 served as the validation set. Patients were divided into ARDS and non-ARDS groups according to whether ARDS occurred within 14 days of admission. Clinical and laboratory data were collected and analyzed. Results: In the training set, 74 patients (26.43%) developed ARDS. Multivariate analysis identified age, history of alcohol consumption, lactate (Lac), red cell distribution width (RDW), fasting blood glucose (FBG), and procalcitonin (PCT) as independent risk factors, while albumin (ALB) was a protective factor. These variables were incorporated into the nomogram. The area under the ROC curve (AUC) was 0.899 in internal validation and 0.927 in external validation. Hosmer-Lemeshow tests demonstrated good calibration in both cohorts (P > 0.05). Decision curve analysis indicated favorable clinical net benefit across a wide range of threshold probabilities. Conclusion: Age, alcohol consumption history, Lac, RDW, ALB, FBG, and PCT are key predictors of ARDS in patients with AP. The proposed nomogram demonstrates good discrimination, calibration, and clinical utility, and may assist clinicians in early risk stratification.
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