Department of Gastroenterology and Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China
Alpha Genomics Private Limited, Islamabad, Pakistan
Pol Arch Intern Med. 2024 May 28;134(5). doi: 10.20452/pamw.16700. Epub 2024 Mar 15.
Acute pancreatitis (AP) that progresses to persistent organ failure is referred to as severe acute pancreatitis (SAP). It is a condition associated with a relatively high mortality. A prediction model that would facilitate early recognition of patients at risk for SAP is crucial for improvement of patient prognosis.
The aim of this study was to evaluate the accuracy of extreme gradient boosting (XGBoost) and artificial neural network (ANN) models for predicting SAP.
A total of 648 patients with AP were enrolled. XGBoost and ANN models were developed and validated in the training (519 patients) and test sets (129 patients). The accuracy and predictive performance of the XGBoost and ANN models were evaluated using both the area under the receiver operating characteristic curves (AUCs) and the area under the precision‑recall curves (AUC‑PRs).
A total of 15 variables were selected for model construction through a univariable analysis. The AUCs of the XGBoost and ANN models in 5‑fold cross‑validation of the training set were 0.92 (95% CI, 0.87-0.97) and 0.86 (95% CI, 0.78-0.92), respectively, whereas the AUCs for the test set were 0.93 (95% CI, 0.85-1) and 0.87 (95% CI, 0.79-0.96), respectively. The XGBoost model outperformed the ANN model in terms of both diagnostic accuracy and AUC‑PR. Individual predictions of the XGBoost model were explained using a local interpretable model‑agnostic explanation plot.
An interpretable XGBoost model showed better discriminatory efficiency for predicting SAP than the ANN model, and could be used in clinical practice to identify patients at risk for SAP.
进展为持续性器官衰竭的胰腺炎（AP）被称为重症急性胰腺炎（SAP）。这种疾病的死亡率相对较高。开发一种有助于早期识别 SAP 风险患者的预测模型对于改善患者预后至关重要。
本研究旨在评估极端梯度增强（XGBoost）和人工神经网络（ANN）模型预测 SAP 的准确性。
共纳入 648 例 AP 患者。在训练集（519 例患者）和测试集（129 例患者）中分别建立和验证了 XGBoost 和 ANN 模型。通过接受者操作特征曲线下面积（AUCs）和精度-召回曲线下面积（AUC-PRs）评估 XGBoost 和 ANN 模型的准确性和预测性能。
通过单变量分析共选择了 15 个变量用于模型构建。训练集 5 折交叉验证中 XGBoost 和 ANN 模型的 AUC 分别为 0.92（95%可信区间，0.87-0.97）和 0.86（95%可信区间，0.78-0.92），而测试集的 AUC 分别为 0.93（95%可信区间，0.85-1）和 0.87（95%可信区间，0.79-0.96）。XGBoost 模型在诊断准确性和 AUC-PR 方面均优于 ANN 模型。XGBoost 模型的个体预测结果使用局部可解释模型无关解释图进行了解释。
可解释的 XGBoost 模型在预测 SAP 方面的判别效率优于 ANN 模型，可用于临床实践中识别 SAP 风险患者。
