ArticleInternational journal of environmental research and public health2018
Identification of Pancreatic Injury in Patients with Elevated Amylase or Lipase Level Using a Decision Tree Classifier: A Cross-Sectional Retrospective Analysis in a Level I Trauma Center.
Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- The association between pancreatic enzyme serum concentrations and blunt pancreatic trauma: a systematic review.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2026Pooled it
- Development of a Donor-Based Predictive Model for Pancreas Non-Utilization Among Deceased Donor Transplants in the US.Clinical transplantation · 2026Article
- Interpretable multitemporal liver function indicator model for prediction and risk factor analysis of drug induced liver injury.Scientific reports · 2024Article
- Analysis of anterior segment in primary angle closure suspect with deep learning models.BMC medical informatics and decision making · 2024Article
- Delayed distal pancreatectomy for isolated complete pancreatic disruption secondary to "trivial" blunt abdominal injury: A case report and literature review.Clinical case reports · 2022Article
- Traumatic Pancreatic Injury Presentation, Management, and Outcome: An Observational Retrospective Study From a Level 1 Trauma Center.Frontiers in surgery · 2021Article
- A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters.Frontiers in oncology · 2021Article
- Isolated Pancreatic Tail Injury in Paediatrics; A Case Report and Literature Review.Bulletin of emergency and trauma · 2020Article
- Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features.European radiology · 2019Article
- Duodeno-pancreatic and extrahepatic biliary tree trauma: WSES-AAST guidelines.World journal of emergency surgery : WJES · 2019Review
- A Deep CNN-LSTM Model for Particulate Matter (PMSensors (Basel, Switzerland) · 2018Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn trauma patients, pancreatic injury is rare; however, if undiagnosed, it is associated with high morbidity and mortality rates. Few predictive models are available for the identification of pancreatic injury in trauma patients with elevated serum pancreatic enzymes. In this study, we aimed to construct a model for predicting pancreatic injury using a decision tree (DT) algorithm, along with data obtained from a population-based trauma registry in a Level I trauma center.
methodsA total of 991 patients with elevated serum levels of amylase (>137 U/L) or lipase (>51 U/L), including 46 patients with pancreatic injury and 865 without pancreatic injury between January 2009 and December 2016, were allocated in a ratio of 7:3 to training (n = 642) or test (n = 269) sets. Using the data on patient and injury characteristics as well as laboratory data, the DT algorithm with Classification and Regression Tree (CART) analysis was performed based on the Gini impurity index, using the rpart function in the rpart package in R.
resultsAmong the trauma patients with elevated amylase or lipase levels, three groups of patients were identified as having a high risk of pancreatic injury, using the DT model. These included (1) 69% of the patients with lipase level ≥306 U/L; (2) 79% of the patients with lipase level between 154 U/L and 305 U/L and shock index (SI) ≥ 0.72; and (3) 80% of the patients with lipase level <154 U/L with abdomen injury, glucose level <158 mg/dL, amylase level <90 U/L, and neutrophil percentage ≥76%; they had all sustained pancreatic injury. With all variables in the model, the DT achieved an accuracy of 97.9% (sensitivity of 91.4% and specificity of 98.3%) for the training set. In the test set, the DT achieved an accuracy of 93.3%, sensitivity of 72.7%, and specificity of 94.2%.
conclusionsWe established a DT model using lipase, SI, and additional conditions (injury to the abdomen, glucose level <158 mg/dL, amylase level <90 U/L, and neutrophils ≥76%) as important nodes to predict three groups of patients with a high risk of pancreatic injury. The proposed decision-making algorithm may help in identifying pancreatic injury among trauma patients with elevated serum amylase or lipase levels.
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