Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital.
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
J Clin Gastroenterol. 2024 Aug 1;58(7):692-701. doi: 10.1097/MCG.0000000000001909.
Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. To assess the value of the Modified Computed Tomography Severity Index (MCTSI) combined with serological indicators for early prediction of severe acute pancreatitis (SAP) by automated ML (AutoML).
The clinical data, of the patients with acute pancreatitis (AP) hospitalized in Hospital 1 and hospital 2 from January 2017 to December 2021, were retrospectively analyzed. Serological indicators within 24 hours of admission were collected. MCTSI score was completed by noncontrast computed tomography within 24 hours of admission. Data from the hospital 1 were adopted for training, and data from the hospital 2 were adopted for external validation. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of AP. Models were built using traditional logistic regression and AutoML analysis with 4 types of algorithms. The performance of models was evaluated by the receiver operating characteristic curve, the calibration curve, and the decision curve analysis based on logistic regression and decision curve analysis, feature importance, SHapley Additive exPlanation Plot, and Local Interpretable Model Agnostic Explanation based on AutoML.
A total of 499 patients were used to develop the models in the training data set. An independent data set of 201 patients was used to test the models. The model developed by the Deep Neural Net (DL) outperformed other models with an area under the receiver operating characteristic curve (areas under the curve) of 0.907 in the test set. Furthermore, among these AutoML models, the DL and gradient boosting machine models achieved the highest sensitivity values, both exceeding 0.800.
The AutoML model based on the MCTSI score combined with serological indicators has good predictive value for SAP in the early stage.
机器学习（ML）算法由于其强大的学习和泛化能力，广泛应用于医学模型的构建。本研究旨在通过自动化机器学习（AutoML）评估改良 CT 严重指数（MCTSI）联合血清学指标对急性胰腺炎（AP）患者早期预测重症急性胰腺炎（SAP）的价值。
回顾性分析 2017 年 1 月至 2021 年 12 月在医院 1 和医院 2 住院的急性胰腺炎（AP）患者的临床资料，收集入院 24 小时内的血清学指标，入院 24 小时内行非增强 CT 检查完成 MCTSI 评分。医院 1 的数据用于训练，医院 2 的数据用于外部验证。AP 和 SAP 的诊断依据 2012 年亚特兰大修订版 AP 分类。采用传统逻辑回归和 4 种算法的 AutoML 分析建立模型。基于逻辑回归和决策曲线分析，利用受试者工作特征曲线、校准曲线和决策曲线分析评估模型性能，基于 AutoML 的特征重要性、SHapley Additive exPlanation Plot 和 Local Interpretable Model Agnostic Explanation 进行模型解释。
在训练数据集中，共纳入 499 例患者建立模型，在独立的 201 例患者数据集中对模型进行测试。在测试集中，深度神经网络（DL）模型的曲线下面积（AUC）最高，为 0.907。此外，在这些 AutoML 模型中，DL 和梯度提升机模型的敏感性最高，均超过 0.800。
基于 MCTSI 评分和血清学指标的 AutoML 模型对 SAP 早期具有良好的预测价值。
