Department of Gastroenterology, Daping Hospital, Army Medical University, Chongqing 400042, China.
Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing 400042, China.
Mediators Inflamm. 2021 May 3;2021:5525118. doi: 10.1155/2021/5525118. eCollection 2021.
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.
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.
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.
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).
多器官功能衰竭（MOF）可能导致中度重症（MSAP）或重症急性胰腺炎（SAP）的死亡率增加。本研究旨在使用机器学习预测疾病过程中 MOF 的风险。
通过单因素分析筛选出患者有无 MOF 之间有显著差异的临床和实验室特征。通过六种机器学习方法为选定特征开发预测模型。通过五重交叉验证对模型进行内部验证，并在相应模型中生成一系列最优特征子集。使用测试集评估六种模型的预测性能。
在 455 例 MSAP 或 SAP 患者中，有 305 例（68%）发生 MOF。在训练和验证集中，将 MOF 组与无 MOF 组之间存在显著差异的 18 个特征用于建模。白细胞介素-6 水平、肌酐水平和动力学时间是 K 折交叉验证中选择的最优特征子集中的三个最重要特征。自适应增强算法（AdaBoost）表现出最佳的预测性能，AUC 值最高（0.826；95%置信区间：0.740 至 0.888）。在测试集中，AdaBoost 的敏感性（80.49%）和逻辑回归分析的特异性（93.33%）是六种模型中最好的得分。
基于机器学习成功开发了预测 MSAP 或 SAP 合并 MOF 的模型。通过测试集评估了预测性能，其中 AdaBoost 表现出令人满意的预测性能。该研究在中国临床试验注册中心（注册号：ChiCTR1800016079）注册。
