Research Paper Volume 13, Issue 6 pp 8817—8834
Accurate prediction of acute pancreatitis severity with integrative blood molecular measurements
- 1 Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- 2 Translational Medicine Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- 3 Singlera Genomics Inc., San Diego, CA 92037, USA
- 4 Key Laboratory of Intelligent Critical Care and Life Support Research of Zhejiang Province, Department of Intensive Care, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
Received: July 2, 2020 Accepted: February 1, 2021 Published: March 10, 2021
https://doi.org/10.18632/aging.202689How to Cite
Copyright: © 2021 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Early 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.
Results: We 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).
Conclusions: We 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 Methods: Nine 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.
Abbreviations
AP: acute pancreatitis; MAP: mild acute pancreatitis; SAP: severe acute pancreatitis.