ArticleBMC nephrology2023
Construction and validation of a risk assessment model for acute kidney injury in patients with acute pancreatitis in the intensive care unit.
Article in BMC nephrology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Risk prediction models for acute kidney injury in patients with acute pancreatitis: a systematic review.BMC gastroenterology · 2026Pooled it
- Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Article
- Glucose-to-albumin ratio predicts short-term mortality in critically ill patients with acute pancreatitis.Scientific reports · 2026Article
- Prognostic value of inflammatory biochemical markers (IL-8, PCT, CRP) for cardiovascular disease in patients with pancreatitis.Journal of medical biochemistry · 2026Article
- Relationship between intra-bladder pressure and acute kidney injury in patients with acute pancreatitis: interpretable machine learning approach.BMC nephrology · 2025Article
- Review
- Article
- Association between the triglyceride-glucose index and the risk of acute kidney injury in critically ill patients with acute pancreatitis: a retrospective study.Journal of intensive care · 2025Article
- The predictive value of the serum creatinine-to-albumin ratio (sCAR) and lactate dehydrogenase-to-albumin ratio (LAR) in sepsis-related persistent severe acute kidney injury.European journal of medical research · 2025Article
- The difference in hematocrit and albumin levels and the risk of sepsis for patients with acute pancreatitis: a retrospective cohort study based on the MIMIC-IV database.Sao Paulo medical journal = Revista paulista de medicina · 2025Article
- Association between serum creatinine to albumin ratio and short- and long-term all-cause mortality in patients with acute pancreatitis admitted to the intensive care unit: a retrospective analysis based on the MIMIC-IV database.Frontiers in immunology · 2024Article
- Interpretable machine learning for early prediction of acute kidney injury in critically ill patients with acute pancreatitis.Digital healthArticle
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo construct and validate a risk assessment model for acute kidney injury (AKI) in patients with acute pancreatitis (AP) in the intensive care unit (ICU).
methodsA total of 963 patients diagnosed with acute pancreatitis (AP) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database was included. These patients were randomly divided into training set (N = 674) and validation set (N = 289) at a ratio of 7:3. Clinical characteristics were utilized to establish a nomogram for the prediction of AKI during ICU stay. These variables were selected by the least absolute shrinkage and selection operation (LASSO) regression and included in multivariate logistic regression analysis. Variables with P-values less than 0.05 were included in the final model. A nomogram was constructed based on the final model. The predicted accuracy of the nomogram was assessed by calculating the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Moreover, calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) were performed to evaluate model performance. Decision curve analysis (DCA) evaluated the clinical net benefit of the model.
resultsA multivariable model that included 6 variables: weight, SOFA score, white blood cell count, albumin, chronic heart failure, and sepsis. The C-index of the nomogram was 0.82, and the area under the receiver operating characteristic curve (AUC) of the training set and validation set were 0.82 (95% confidence interval:0.79-0.86) and 0.76 (95% confidence interval: 0.70-0.82), respectively. Calibration plots showed good consistency between predicted and observed outcomes in both the training and validation sets. DCA confirmed the clinical value of the model and its good impact on actual decision-making.
conclusionWe identified risk factors associated with the development of AKI in patients with AP. A risk prediction model for AKI in ICU patients with AP was constructed, and improving the treatment strategy of relevant factors in the model can reduce the risk of AKI in AP patients.
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