Prediction of Multiple Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine Learning: A Multicenter Cohort Study. (4th May 2021)

• Record Type: Journal Article
• Authors: Xu, Fumin; Chen, Xiao; Li, Chenwenya; Liu, Jing; Qiu, Qiu; He, Mi; Xiao, Jingjing; Liu, Zhihui; Ji, Bingjun; Chen, Dongfeng; Liu, Kaijun
• Other Names: Giovarelli Mirella Academic Editor.
• Is Part Of: Mediators of inflammation. Volume 2021(2021)
• Publication Date: 2021-05-04
• Subjects: Inflammation -- Mediators -- Periodicals; Biological response modifiers -- Periodicals; Inflammation (Pathologie) -- Médiateurs; Immunomodulateurs; Biological response modifiers; Inflammation -- Mediators; Immunology; Autacoids; Immunologic Factors; Cell Adhesion Molecules; Cell Communication; Cytokines; Inflammation; Periodicals; Electronic journals; 616.0473
• DOI: 10.1155/2021/5525118
• Languages: English
• ISSNs: 0962-9351

Abstract

Abstract : Background . Multiple organ failure (MOF) may lead to an increased mortality rate of moderately severe (MSAP) or severe acute pancreatitis (SAP). This study is aimed to use machine learning to predict the risk of MOF in the course of disease. Methods . Clinical and laboratory features with significant differences between patients with and without MOF were screened out by univariate analysis. Prediction models were developed for selected features through six machine learning methods. The models were internally validated with a five-fold cross-validation, and a series of optimal feature subsets were generated in corresponding models. A test set was used to evaluate the predictive performance of the six models. Results . 305 (68%) of 455 patients with MSAP or SAP developed MOF. Eighteen features with significant differences between the group with MOF and without it in the training and validation set were used for modeling. Interleukin-6 levels, creatinine levels, and the kinetic time were the three most important features in the optimal feature subsets selected by K-fold cross-validation. The adaptive boosting algorithm (AdaBoost) showed the best predictive performance with the highest AUC value (0.826; 95% confidence interval: 0.740 to 0.888). The sensitivity of AdaBoost (80.49%) and specificity of logistic regression analysis (93.33%) were the best scores among the six models in the test set. Conclusions . A predictive model of MOF complicated by MSAP or SAP was successfully developed based on machine learning. The predictive performance was evaluated by a test set, for which AdaBoost showed a satisfactory predictive performance. The study is registered with the China Clinical Trial Registry (Identifier: ChiCTR1800016079 ).