ArticleRenal failure2024
Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database.
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 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 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
- Research Topics and Trends in MIMIC-IV: A Large ICU Database Relevant for Critical Care Nursing.Nursing in critical care · 2026Review
- Early prediction of bowel necrosis in incarcerated groin hernia using parsimonious machine learning models: a retrospective cohort study.Hernia : the journal of hernias and abdominal wall surgery · 2026Article
- Interpretable machine learning reveals phosphorus-to-albumin ratio as a novel predictor of mortality and acute kidney injury in critically ill pancreatitis patients: a multi-center retrospective analysis.European journal of medical research · 2026Article
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- Construction and validation of a 28-day mortality prediction model for refeeding syndrome in critically ill patients.Frontiers in nutrition · 2026Article
- 3D vascular mapping reveals multi-organ injury following severe acute pancreatitis.Theranostics · 2026Article
- Association of red blood cell distribution width combined with alanine aminotransferase with gastrointestinal hemorrhage in patients with acute pancreatitis: a retrospective MIMIC-IV study.Frontiers in medicine · 2026Article
- Multicenter machine learning study for long-term prediction of acute kidney injury after complete mesocolic excision: integrating inflammatory biomarkers and transfusion-related risk factors.Frontiers in oncology · 2026Article
- Prediction of intraoperative acquired pressure injury in elderly cardiac surgery patients via machine learning: model development with the MIMIC-IV and external validation.BMC medical informatics and decision making · 2025Article
- Development and validation of an explainable machine learning model for predicting acute kidney injury after robot-assisted partial nephrectomy: a retrospective multicenter study.BMC nephrology · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Artificial intelligence, machine learning, telemedicine, and digital transformation in nephrology and transplantation.Renal failure · 2025Article
- Machine learning-based prediction of short-term outcomes in aneurysmal subarachnoid hemorrhage: a multicenter study integrating clinical and inflammatory indicators.BMC medicine · 2025Article
- Review
- AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis.European journal of medical research · 2025Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP.
methodsPatients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation.
resultsWe analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM's performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903).
conclusionsThe GBM model's precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care.
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