Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, China.
Department of Cardiothoracic Surgery, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, China.
BMC Gastroenterol. 2023 Sep 13;23(1):310. doi: 10.1186/s12876-023-02949-3.
To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study.
Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree XGBoost model. Finally, the simple-tree XGBoost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.
Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree XGBoost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree XGBoost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P < 0.001).
The simple-tree XGBoost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection.
通过回顾性病例对照研究,使用 XGBoost 算法评估失代偿期肝硬化(DC)患者感染的有效预测因子。
回顾性收集了 5 家三级医院收治的 6648 例 DC 患者的临床资料。通过单因素分析和最小绝对值收缩和选择算子(LASSO)回归确定有显著差异的指标。进一步采用多树极端梯度提升(XGBoost)机器学习基础模型对 LASSO 选择的特征重要性进行排序,然后构建基于简单树 XGBoost 模型的感染风险预测模型。最后,将简单树 XGBoost 模型与传统逻辑回归(LR)模型进行比较。通过接受者操作特征曲线下面积(AUROC)、灵敏度和特异性评估模型的性能。
筛选出总胆红素、血钠、白蛋白、凝血酶原活动度、白细胞计数和中性粒细胞与淋巴细胞比值 6 个特征作为 DC 患者感染的预测因子。由这些特征构建的简单树 XGBoost 模型可以准确预测感染风险,在训练集、内部验证集和外部特征集的 AUROC 分别为 0.971、0.915 和 0.900。在训练集、内部验证集和外部特征集上,简单树 XGBoost 模型的性能均优于传统 LR 模型(P<0.001)。
基于有限的临床数据和医疗资源,为 DC 患者开发的简单树 XGBoost 预测模型有助于基层医疗保健人员及时识别潜在感染。