Liu Jie, Zhang Jian, Huang Haodong, Wang Yunting, Zhang Zuyue, Ma Yunfeng, He Xiangqian
School of Medical Informatics, Chongqing Medical University, Chongqing, China.
Medical Data Science Academy, Chongqing Medical University, Chongqing, China.
Front Pediatr. 2021 Nov 8;9:756095. doi: 10.3389/fped.2021.756095. eCollection 2021.
We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms. A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models. In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067-1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270-1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008-1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996-1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575). The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.
我们探讨了川崎病（KD）患儿静脉注射免疫球蛋白（IVIG）抵抗的危险因素，并基于机器学习算法构建了预测模型。对2015年1月至2020年8月在重庆医科大学7家附属医院住院的1398例KD患者进行了回顾性研究。所有患者分为IVIG反应组和IVIG抵抗组，并随机分为训练集和验证集。采用逻辑回归分析确定独立危险因素。构建了逻辑回归列线图、支持向量机（SVM）、XGBoost和LightGBM预测模型，并与先前的模型进行比较。1398例患者中，1240例为IVIG反应者，158例对IVIG抵抗。根据训练集的逻辑回归分析结果，确定了4个独立危险因素，包括总胆红素（TBIL）（OR = 1.115，95%CI 1.067 - 1.165）、降钙素原（PCT）（OR = 1.511，95%CI 1.270 - 1.798）、谷丙转氨酶（ALT）（OR = 1.013，95%CI 1.008 - 1.018）和血小板计数（PLT）（OR = 0.998，95%CI 0.996 - 1）。基于上述独立危险因素构建了逻辑回归列线图、SVM、XGBoost和LightGBM预测模型。敏感性分别为0.617、0.681、0.638和0.702，特异性分别为0.712、0.841、0.967和0.903，曲线下面积（AUC）分别为0.731、0.814、0.804和0.874。在预测模型中，LightGBM模型显示出最佳的综合预测能力，AUC为0.874，超过了先前的经典模型，如Egami（AUC = 0.581）、Kobayashi（AUC = 0.524）、Sano（AUC = 0.519）、Fu（AUC = 0.578）和Formosa（AUC = 0.575）。用于IVIG抵抗KD患者的机器学习LightGBM预测模型优于先前的模型。我们的研究结果可能有助于早期识别IVIG抵抗风险并改善其预后。
