ArticleAging2021
Accurate prediction of acute pancreatitis severity with integrative blood molecular measurements.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Clinical usefulness of scoring systems to predict severe acute pancreatitis: A systematic review and meta-analysis with pre and post-test probability assessment.United European gastroenterology journal · 2023Pooled it
- Acute pancreatitis: mechanisms and therapeutic approaches.Signal transduction and targeted therapy · 2026Review
- Electrophysiological biomarkers for foundational signal markers in chronic pancreatitis using machine learning models.Frontiers in pain research (Lausanne, Switzerland) · 2026Article
- Constructing a prediction model for acute pancreatitis severity based on liquid neural network.Scientific reports · 2025Article
- Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis.Heliyon · 2024Article
- Predictive Value of Troponin I, Creatinine Kinase Isoenzyme and the New Japanese Severity Score in Severe Acute Pancreatitis.Patient preference and adherence · 2024Article
- Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning.Journal of personalized medicine · 2022Article
- Identification of novel immune-related targets mediating disease progression in acute pancreatitis.Frontiers in cellular and infection microbiology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly diagnosis of severe acute pancreatitis (SAP) is essential to minimize its mortality and improve prognosis. We aimed to develop an accurate and applicable machine learning predictive model based on routine clinical testing results for stratifying acute pancreatitis (AP) severity.
resultsWe identified 11 markers predictive of AP severity and trained an AP stratification model called APSAVE, which classified AP cases within 24 hours at an average area under the curve (AUC) of 0.74 +/- 0.04. It was further validated in 568 validation cases, achieving an AUC of 0.73, which is similar to that of Ranson's criteria (AUC = 0.74) and higher than APACHE II and BISAP (AUC = 0.69 and 0.66, respectively).
conclusionsWe developed and validated a venous blood marker-based AP severity stratification model with higher accuracy and broader applicability, which holds promises for reducing SAP mortality and improving its clinical outcomes. MATERIALS AND
methodsNine hundred and forty-five AP patients were enrolled into this study. Clinical venous blood tests covering 65 biomarkers were performed on AP patients within 24 hours of admission. An SAP prediction model was built with statistical learning to select biomarkers that are most predictive for AP severity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.