ArticleRenal failure2024
An early warning model for predicting major adverse kidney events within 30 days in acute pancreatitis patients.
Article in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- An interpretable XGboost algorithm for predicting 30-day mortality in acute pancreatitis using routine biomarkers.BMC medical research methodology · 2026Article
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4 authors.
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Abstract
backgroundRenal impairment is a common outcome of acute pancreatitis. Nevertheless, research on predictive models for major adverse kidney events within 30 days (MAKE30) in acute pancreatitis (AP) has been scarce.
methodsA retrospective study was conducted at Gansu Provincial Hospital, involving 391 patients with acute pancreatitis who were categorized into non-MAKE30 (320 cases) and MAKE30 (71 cases) groups. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for MAKE30 in the aforementioned patient cohort. The nomogram was developed utilizing findings from a multivariate logistic regression analysis. Subsequent evaluation of the nomogram involved the use of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Additionally, subgroup analyses were performed to different AP etiology and assess secondary outcomes.
resultsGender, respiratory rate (RR), creatinine (Cr), interleukin 6 (IL-6), prothrombin time (PT), and cardiovascular disease (CVD) were identified as associated predictors of Major Adverse Kidney Events within 30 days (MAKE30) in patients with acute pancreatitis. A nomogram model was developed based on these predictors. Evaluation using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA) demonstrated that the nomogram model exhibited significant discrimination (AUC = 0.842) > the SOFA score (AUC = 0.809), excellent calibration, and substantial clinical utility. Subgroup analysis showed the nomogram model provided good predictive value for both secondary outcomes and various etiologies.
conclusionThis model shows promise in efficiently and accurately evaluating the risk of developing MAKE30 in acute pancreatitis patients within the first 24 h of hospitalization.
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