目的:在回顾性病例对照研究中,采用XGBoost算法评估失代偿期肝硬化(DC)患者感染的有效预测因子。
方法:回顾性收集5家三级医院收治的6,648例DC患者的临床资料。通过单变量分析和最小绝对收缩和选择算子(LASSO)回归确定具有显着差异的指标。进一步使用基于多树极端梯度增强(XGBoost)机器学习的模型对从LASSO中选择的特征的重要性进行排名,并随后使用简单树XGBoost模型构建感染风险预测模型。最后,将简单树XGBoost模型与传统逻辑回归(LR)模型进行了比较。通过受试者工作特征曲线下面积(AUROC)评估模型的性能,灵敏度,和特异性。
结果:六个功能,包括总胆红素,血钠,白蛋白,凝血酶原活性,白细胞计数,选择中性粒细胞与淋巴细胞的比率作为DC患者感染的预测因子。通过这些特征进行的简单树XGBoost模型可以准确预测感染风险,训练集中的AUROC为0.971,灵敏度为0.915,特异性为0.900。简单树XGBoost模型在训练集上的性能优于传统LR模型,内部验证集,和外部特征集(P<0.001)。
结论:基于医疗资源有限的DC患者可获得的最少量临床数据开发的简单树XGBoost预测模型可以帮助初级保健医生及时识别潜在的感染。
OBJECTIVE: To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective
case-control study.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.