Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China.
Front Cell Infect Microbiol. 2022 Jun 10;12:893294. doi: 10.3389/fcimb.2022.893294. eCollection 2022.
This study aimed to develop an interpretable random forest model for predicting severe acute pancreatitis (SAP).
Clinical and laboratory data of 648 patients with acute pancreatitis were retrospectively reviewed and randomly assigned to the training set and test set in a 3:1 ratio. Univariate analysis was used to select candidate predictors for the SAP. Random forest (RF) and logistic regression (LR) models were developed on the training sample. The prediction models were then applied to the test sample. The performance of the risk models was measured by calculating the area under the receiver operating characteristic (ROC) curves (AUC) and area under precision recall curve. We provide visualized interpretation by using local interpretable model-agnostic explanations (LIME).
The LR model was developed to predict SAP as the following function: -1.10-0.13×albumin (g/L) + 0.016 × serum creatinine (μmol/L) + 0.14 × glucose (mmol/L) + 1.63 × pleural effusion (0/1)(No/Yes). The coefficients of this formula were utilized to build a nomogram. The RF model consists of 16 variables identified by univariate analysis. It was developed and validated by a tenfold cross-validation on the training sample. Variables importance analysis suggested that blood urea nitrogen, serum creatinine, albumin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, calcium, and glucose were the most important seven predictors of SAP. The AUCs of RF model in tenfold cross-validation of the training set and the test set was 0.89 and 0.96, respectively. Both the area under precision recall curve and the diagnostic accuracy of the RF model were higher than that of both the LR model and the BISAP score. LIME plots were used to explain individualized prediction of the RF model.
An interpretable RF model exhibited the highest discriminatory performance in predicting SAP. Interpretation with LIME plots could be useful for individualized prediction in a clinical setting. A nomogram consisting of albumin, serum creatinine, glucose, and pleural effusion was useful for prediction of SAP.
本研究旨在开发一种用于预测重症急性胰腺炎（SAP）的可解释随机森林模型。
回顾性分析了 648 例急性胰腺炎患者的临床和实验室数据，并按 3:1 的比例随机分配到训练集和测试集中。采用单因素分析筛选 SAP 的候选预测因子。在训练样本上建立随机森林（RF）和逻辑回归（LR）模型。然后将预测模型应用于测试样本。通过计算受试者工作特征（ROC）曲线下面积（AUC）和精度召回曲线下面积来衡量风险模型的性能。我们通过使用局部可解释模型不可知解释（LIME）提供可视化解释。
建立了预测 SAP 的 LR 模型，其函数如下：-1.10-0.13×白蛋白（g/L）+0.016×血清肌酐（μmol/L）+0.14×血糖（mmol/L）+1.63×胸腔积液（0/1）（无/有）。该公式的系数用于构建列线图。RF 模型由单因素分析确定的 16 个变量组成。它是在训练样本上通过十折交叉验证开发和验证的。变量重要性分析表明，血尿素氮、血清肌酐、白蛋白、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇、钙和血糖是 SAP 最重要的七个预测因子。在训练集和测试集的十折交叉验证中，RF 模型的 AUC 分别为 0.89 和 0.96。RF 模型的精度召回曲线下面积和诊断准确性均高于 LR 模型和 BISAP 评分。使用 LIME 图解释 RF 模型的个体化预测。
可解释的 RF 模型在预测 SAP 方面表现出最高的判别性能。使用 LIME 图进行解释对于临床环境中的个体化预测可能是有用的。由白蛋白、血清肌酐、血糖和胸腔积液组成的列线图可用于预测 SAP。
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