Sun Hong-Wei, Lu Jing-Yi, Weng Yi-Xin, Chen Hao, He Qi-Ye, Liu Rui, Li Hui-Ping, Pan Jing-Ye, Shi Ke-Qing
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Translational Medicine Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Aging (Albany NY). 2021 Mar 10;13(6):8817-8834. doi: 10.18632/aging.202689.
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.
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).
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.
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.
早期诊断重症急性胰腺炎（SAP）对于降低其死亡率和改善预后至关重要。我们旨在基于常规临床检测结果开发一种准确且适用的机器学习预测模型，用于对急性胰腺炎（AP）的严重程度进行分层。
我们确定了11个预测AP严重程度的标志物，并训练了一个名为APSAVE的AP分层模型，该模型在24小时内对AP病例进行分类，平均曲线下面积（AUC）为0.74±0.04。它在568例验证病例中得到进一步验证，AUC为0.73，与兰森标准（AUC = 0.74）相似，高于急性生理与慢性健康状况评分系统II（APACHE II）和床边指数（BISAP）（分别为AUC = 0.69和0.66）。
我们开发并验证了一种基于静脉血标志物的AP严重程度分层模型，具有更高的准确性和更广泛的适用性，有望降低SAP死亡率并改善其临床结局。
945例AP患者纳入本研究。在AP患者入院24小时内进行了涵盖65种生物标志物的临床静脉血检测。使用统计学习构建了一个SAP预测模型，以选择对AP严重程度最具预测性的生物标志物。
