Li Lin, Wang Dong, Yang Rongrong, Liao Xing, Wu Ling
Department of Infectious Diseases, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), National Regional Medical Center, Fujian Medical University, Fuzhou, 350014, PR China.
Ital J Pediatr. 2025 Mar 24;51(1):95. doi: 10.1186/s13052-025-01934-8.
To establish a decision tree model of Mycoplasma pneumoniae pneumonia(MPP) complicated with plastic bronchitis(PB) in children, and to explore the application value of decision tree model in the auxiliary diagnosis of children.
A retrospective study was conducted to collect the clinical data of 214 children who met the admission criteria in Fujian Children's Hospital from June 2022 to June 2024, and they were divided into plastic bronchitis group (n = 66) and non-plastic bronchitis group (n = 148). Using R language, 70% of the data from each group of patients was randomly selected for training the model using decision tree algorithm analysis, thus generating a clinical diagnostic decision tree for Mycoplasma pneumoniae (MP) combined with PB. The generated decision tree model was validated on the validation sample dataset and the detection effect value of the model was calculated.
In this study, a total of 22 indicators were employed to build the decision tree diagnostic model. Univariate statistical analysis was carried out prior to the model construction, and it was discovered that the differences of 13 indicators between the molded group and the non-molded group were statistically significant. A decision tree model with D-dimer ≥ 1.7ug/mL, C-reactive protein ≥ 15 mg/L, drug resistance or not, and serum ferritin<137 mg/L was constructed in the training sample dataset of the molded group and the non-molded group. The sensitivity of the decision tree model was 0.884, which was verified in the dataset of the remolded group and the non-molded group. The specificity was 0.727, and the area under the receiver operating characteristic curve was 0.831.
Decision tree model can provide reference for the application of auxiliary diagnosis in children with mycoplasma pneumoniae pneumonia complicated with plastic bronchitis. The model has good discriminative ability in general, and is worthy of clinical application and further study.
建立儿童支原体肺炎(MPP)合并塑型支气管炎(PB)的决策树模型,探讨决策树模型在儿童辅助诊断中的应用价值。
采用回顾性研究方法,收集2022年6月至2024年6月在福建儿童医院符合入院标准的214例儿童临床资料,分为塑型支气管炎组(n = 66)和非塑型支气管炎组(n = 148)。利用R语言,从每组患者数据中随机抽取70%,采用决策树算法分析训练模型,生成支原体肺炎(MP)合并PB的临床诊断决策树。将生成的决策树模型在验证样本数据集上进行验证,并计算模型的检测效应值。
本研究共采用22项指标构建决策树诊断模型。在模型构建前进行单因素统计分析,发现成型组与未成型组13项指标差异有统计学意义。在成型组和未成型组的训练样本数据集中构建了以D-二聚体≥1.7μg/mL、C反应蛋白≥15mg/L、是否耐药、血清铁蛋白<137mg/L为特征的决策树模型。决策树模型的灵敏度为0.884,在重建模组和未建模组的数据集中得到验证,特异度为0.727,受试者操作特征曲线下面积为0.831。
决策树模型可为儿童支原体肺炎合并塑型支气管炎的辅助诊断应用提供参考。该模型总体判别能力良好,值得临床应用及进一步研究。