Zhou Jintuo, Xie Yongjin, Liu Ying, Niu Peiguang, Chen Huajiao, Zeng Xiaoping, Zhang Jinhua
Department of Pharmacy, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, #18 Daoshan Road, Fuzhou, China.
Department of Obstetrics and Gynecology, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Maternity and Child Health Hospital, Fujian Medical University, #18 Daoshan Road, Fuzhou, China.
Sci Rep. 2025 Apr 2;15(1):11217. doi: 10.1038/s41598-025-91434-w.
Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest for an accurate and efficient method for early DIC prediction is of paramount importance. Candidate predictors encompassed demographics, comorbidities, laboratory findings, and therapy strategies. A stepwise logistic regression model was employed to select the features included in the final model. Six machine learning algorithms-logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), decision tree (DT), and k-nearest neighbor (KNN)-were employed to construct predictive models for DIC in critically ill children. Models were then evaluated by using area under the curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), precision, recall and decision curve analysis (DCA). Interpretation of the optimal model was conducted using shapley additive explanations (SHAP). A total of 6093 critically ill children were encompassed in this study, of whom 681 (11.2%) developed DIC. The RF model exhibited the highest levels of accuracy (0.856), sensitivity (0.866), Kappa (0.472), NPV (0.423), and recall (0.866). However, the XGB model outperformed RF, LR, SVM, DT, and KNN in terms of AUC (0.908), specificity (0.859), PPV (0.978), and precision (0.969). Decision curve analysis (DCA) confirmed the superior clinical utility of the XGB model. Overall, the XGB model demonstrated superior clinical utility compared to RF, LR, SVM, DT, and KNN. We named the final model Alfalfa-PICU-DIC. SHAP analysis identified D-dimer, INR, PT, TT, and PLT count as the top predictors of DIC. Machine learning models can be a reliable tool for predicting DIC in critically ill children, which will facilitate timely intervention, thereby reducing the burden of DIC on patients in the pediatric intensive care unit (PICU).
弥散性血管内凝血（DIC）是一种血栓出血性疾病，在危重症儿童中可能危及生命，因此寻求一种准确有效的早期DIC预测方法至关重要。候选预测指标包括人口统计学特征、合并症、实验室检查结果和治疗策略。采用逐步逻辑回归模型选择最终模型中包含的特征。使用六种机器学习算法——逻辑回归（LR）、极端梯度提升（XGB）、随机森林（RF）、支持向量机（SVM）、决策树（DT）和k近邻（KNN）——构建危重症儿童DIC的预测模型。然后使用曲线下面积（AUC）、准确性、特异性、敏感性、阳性预测值（PPV）、阴性预测值（NPV）、精确率、召回率和决策曲线分析（DCA）对模型进行评估。使用夏普利加性解释（SHAP）对最优模型进行解释。本研究共纳入6093例危重症儿童，其中681例（11.2%）发生DIC。RF模型在准确性（0.856）、敏感性（0.866）、卡帕值（0.472）、NPV（0.423）和召回率（0.866）方面表现最高。然而，XGB模型在AUC（0.908）、特异性（0.859）、PPV（0.978）和精确率（0.969）方面优于RF、LR、SVM、DT和KNN。决策曲线分析（DCA）证实了XGB模型具有更好的临床实用性。总体而言，与RF、LR、SVM、DT和KNN相比，XGB模型具有更好的临床实用性。我们将最终模型命名为苜蓿-PICU-DIC。SHAP分析确定D-二聚体、国际标准化比值（INR）、凝血酶原时间（PT）、凝血酶时间（TT）和血小板计数（PLT）为DIC的主要预测指标。机器学习模型可以成为预测危重症儿童DIC的可靠工具，这将有助于及时干预，从而减轻儿科重症监护病房（PICU）中DIC患者的负担。
